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diff --git a/Advance/opencv-inpainting/opencv_inpainting.ipynb b/Advance/opencv-inpainting/opencv_inpainting.ipynb
new file mode 100644
index 0000000..d7704a7
--- /dev/null
+++ b/Advance/opencv-inpainting/opencv_inpainting.ipynb
@@ -0,0 +1,286 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# USAGE\n",
+    "# python opencv_inpainting.py --image examples/example01.png --mask examples/mask01.png\n",
+    "\n",
+    "# import the necessary packages\n",
+    "import argparse\n",
+    "import cv2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "usage: ipykernel_launcher.py [-h] -i IMAGE -m MASK [-a {telea,ns}] [-r RADIUS]\n",
+      "ipykernel_launcher.py: error: the following arguments are required: -i/--image, -m/--mask\n"
+     ]
+    },
+    {
+     "ename": "SystemExit",
+     "evalue": "2",
+     "output_type": "error",
+     "traceback": [
+      "An exception has occurred, use %tb to see the full traceback.\n",
+      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# construct the argument parser and parse the arguments\n",
+    "ap = argparse.ArgumentParser()\n",
+    "ap.add_argument(\"-i\", \"--image\", type=str, required=True,\n",
+    "\thelp=\"path input image on which we'll perform inpainting\")\n",
+    "ap.add_argument(\"-m\", \"--mask\", type=str, required=True,\n",
+    "\thelp=\"path input mask which corresponds to damaged areas\")\n",
+    "ap.add_argument(\"-a\", \"--method\", type=str, default=\"telea\",\n",
+    "\tchoices=[\"telea\", \"ns\"],\n",
+    "\thelp=\"inpainting algorithm to use\")\n",
+    "ap.add_argument(\"-r\", \"--radius\", type=int, default=3,\n",
+    "\thelp=\"inpainting radius\")\n",
+    "args = vars(ap.parse_args())\n",
+    "\n",
+    "# initialize the inpainting algorithm to be the Telea et al. method\n",
+    "flags = cv2.INPAINT_TELEA\n",
+    "\n",
+    "# check to see if we should be using the Navier-Stokes (i.e.,Bertalmio\n",
+    "# et al.) method for inpainting\n",
+    "if args[\"method\"] == \"ns\":\n",
+    "\tflags = cv2.INPAINT_NS\n",
+    "    \n",
+    "# load the (1) input image (i.e., the image we're going to perform\n",
+    "# inpainting on) and (2) the  mask which should have the same input\n",
+    "# dimensions as the input image -- zero pixels correspond to areas\n",
+    "# that *will not* be inpainted while non-zero pixels correspond to\n",
+    "# \"damaged\" areas that inpainting will try to correct\n",
+    "image = cv2.imread(args[\"image\"])\n",
+    "mask = cv2.imread(args[\"mask\"])\n",
+    "mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\n",
+    "\n",
+    "# perform inpainting using OpenCV\n",
+    "output = cv2.inpaint(image, mask, args[\"radius\"], flags=flags)\n",
+    "\n",
+    "# show the original input image, mask, and output image after\n",
+    "# applying inpainting\n",
+    "cv2.imshow(\"Image\", image)\n",
+    "cv2.imshow(\"Mask\", mask)\n",
+    "cv2.imshow(\"Output\", output)\n",
+    "cv2.waitKey(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Advance/opencv-inpainting/opencv_inpainting.py b/Advance/opencv-inpainting/opencv_inpainting.py
new file mode 100644
index 0000000..bfd8fe4
--- /dev/null
+++ b/Advance/opencv-inpainting/opencv_inpainting.py
@@ -0,0 +1,46 @@
+# USAGE
+# python opencv_inpainting.py --image examples/example01.png --mask examples/mask01.png
+
+# import the necessary packages
+import argparse
+import cv2
+
+# construct the argument parser and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", type=str, required=True,
+	help="path input image on which we'll perform inpainting")
+ap.add_argument("-m", "--mask", type=str, required=True,
+	help="path input mask which corresponds to damaged areas")
+ap.add_argument("-a", "--method", type=str, default="telea",
+	choices=["telea", "ns"],
+	help="inpainting algorithm to use")
+ap.add_argument("-r", "--radius", type=int, default=3,
+	help="inpainting radius")
+args = vars(ap.parse_args())
+
+# initialize the inpainting algorithm to be the Telea et al. method
+flags = cv2.INPAINT_TELEA
+
+# check to see if we should be using the Navier-Stokes (i.e.,Bertalmio
+# et al.) method for inpainting
+if args["method"] == "ns":
+	flags = cv2.INPAINT_NS
+
+# load the (1) input image (i.e., the image we're going to perform
+# inpainting on) and (2) the  mask which should have the same input
+# dimensions as the input image -- zero pixels correspond to areas
+# that *will not* be inpainted while non-zero pixels correspond to
+# "damaged" areas that inpainting will try to correct
+image = cv2.imread(args["image"])
+mask = cv2.imread(args["mask"])
+mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+
+# perform inpainting using OpenCV
+output = cv2.inpaint(image, mask, args["radius"], flags=flags)
+
+# show the original input image, mask, and output image after
+# applying inpainting
+cv2.imshow("Image", image)
+cv2.imshow("Mask", mask)
+cv2.imshow("Output", output)
+cv2.waitKey(0)
\ No newline at end of file
diff --git a/Intermediate/Data Analysis/Data Analysis of Cardio Activities dataset.ipynb b/Intermediate/Data Analysis/Data Analysis of Cardio Activities dataset.ipynb
new file mode 100644
index 0000000..2ab61c1
--- /dev/null
+++ b/Intermediate/Data Analysis/Data Analysis of Cardio Activities dataset.ipynb	
@@ -0,0 +1,1730 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Activity Id</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Route Name</th>\n",
+       "      <th>Distance (km)</th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Average Pace</th>\n",
+       "      <th>Average Speed (km/h)</th>\n",
+       "      <th>Calories Burned</th>\n",
+       "      <th>Climb (m)</th>\n",
+       "      <th>Average Heart Rate (bpm)</th>\n",
+       "      <th>Friend's Tagged</th>\n",
+       "      <th>Notes</th>\n",
+       "      <th>GPX File</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013-12-21 07:39:48</th>\n",
+       "      <td>40fa8105-04cd-491a-a4f6-e32ed2849436</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.34</td>\n",
+       "      <td>36:02</td>\n",
+       "      <td>5:41</td>\n",
+       "      <td>10.56</td>\n",
+       "      <td>454.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2013-12-21-073948.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-02-18 19:09:01</th>\n",
+       "      <td>6b831381-6425-4a66-ada4-7f57f013ea05</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.92</td>\n",
+       "      <td>39:25</td>\n",
+       "      <td>5:42</td>\n",
+       "      <td>10.54</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>67</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>TomTom MySports Watch</td>\n",
+       "      <td>2016-02-18-190901.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-07-18 07:47:00</th>\n",
+       "      <td>1eb83982-20b9-494a-954c-ed4152f01273</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.15</td>\n",
+       "      <td>32:36</td>\n",
+       "      <td>5:18</td>\n",
+       "      <td>11.33</td>\n",
+       "      <td>416.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2013-07-18-074700.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-29 18:36:14</th>\n",
+       "      <td>e7a75592-c649-4c99-96be-ec52d555beca</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.70</td>\n",
+       "      <td>1:07:42</td>\n",
+       "      <td>5:20</td>\n",
+       "      <td>11.25</td>\n",
+       "      <td>901.0</td>\n",
+       "      <td>127</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>TomTom MySports Watch</td>\n",
+       "      <td>2017-08-29-183614.gpx</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Activity Id     Type Route Name  \\\n",
+       "Date                                                                            \n",
+       "2013-12-21 07:39:48  40fa8105-04cd-491a-a4f6-e32ed2849436  Running        NaN   \n",
+       "2016-02-18 19:09:01  6b831381-6425-4a66-ada4-7f57f013ea05  Running        NaN   \n",
+       "2013-07-18 07:47:00  1eb83982-20b9-494a-954c-ed4152f01273  Running        NaN   \n",
+       "2017-08-29 18:36:14  e7a75592-c649-4c99-96be-ec52d555beca  Running        NaN   \n",
+       "\n",
+       "                     Distance (km) Duration Average Pace  \\\n",
+       "Date                                                       \n",
+       "2013-12-21 07:39:48           6.34    36:02         5:41   \n",
+       "2016-02-18 19:09:01           6.92    39:25         5:42   \n",
+       "2013-07-18 07:47:00           6.15    32:36         5:18   \n",
+       "2017-08-29 18:36:14          12.70  1:07:42         5:20   \n",
+       "\n",
+       "                     Average Speed (km/h)  Calories Burned  Climb (m)  \\\n",
+       "Date                                                                    \n",
+       "2013-12-21 07:39:48                 10.56            454.0         33   \n",
+       "2016-02-18 19:09:01                 10.54            473.0         67   \n",
+       "2013-07-18 07:47:00                 11.33            416.0         30   \n",
+       "2017-08-29 18:36:14                 11.25            901.0        127   \n",
+       "\n",
+       "                     Average Heart Rate (bpm)  Friend's Tagged  \\\n",
+       "Date                                                             \n",
+       "2013-12-21 07:39:48                       NaN              NaN   \n",
+       "2016-02-18 19:09:01                     141.0              NaN   \n",
+       "2013-07-18 07:47:00                       NaN              NaN   \n",
+       "2017-08-29 18:36:14                     147.0              NaN   \n",
+       "\n",
+       "                                     Notes               GPX File  \n",
+       "Date                                                               \n",
+       "2013-12-21 07:39:48                    NaN  2013-12-21-073948.gpx  \n",
+       "2016-02-18 19:09:01  TomTom MySports Watch  2016-02-18-190901.gpx  \n",
+       "2013-07-18 07:47:00                    NaN  2013-07-18-074700.gpx  \n",
+       "2017-08-29 18:36:14  TomTom MySports Watch  2017-08-29-183614.gpx  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import pandas under the alias pd\n",
+    "import pandas as pd\n",
+    "# Use the read_csv() function to load the dataset (runkeeper_file) into a variable called df_activities. \n",
+    "# Parse the dates with the parse_dates parameter\n",
+    "# set the index to the Date column using the index_col parameter.\n",
+    "df_activities= pd.read_csv(r\".\\datasets\\cardioActivities.csv\", parse_dates=['Date'], index_col=0)\n",
+    "# Display 3 random rows from df_activities using the sample() method.\n",
+    "df_activities.sample(4)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Activity Id', 'Type', 'Route Name', 'Distance (km)', 'Duration',\n",
+       "       'Average Pace', 'Average Speed (km/h)', 'Calories Burned', 'Climb (m)',\n",
+       "       'Average Heart Rate (bpm)', 'Friend's Tagged', 'Notes', 'GPX File'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_activities.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "DatetimeIndex: 508 entries, 2018-11-11 14:05:12 to 2012-08-22 18:53:54\n",
+      "Data columns (total 13 columns):\n",
+      "Activity Id                 508 non-null object\n",
+      "Type                        508 non-null object\n",
+      "Route Name                  1 non-null object\n",
+      "Distance (km)               508 non-null float64\n",
+      "Duration                    508 non-null object\n",
+      "Average Pace                508 non-null object\n",
+      "Average Speed (km/h)        508 non-null float64\n",
+      "Calories Burned             508 non-null float64\n",
+      "Climb (m)                   508 non-null int64\n",
+      "Average Heart Rate (bpm)    294 non-null float64\n",
+      "Friend's Tagged             0 non-null float64\n",
+      "Notes                       231 non-null object\n",
+      "GPX File                    504 non-null object\n",
+      "dtypes: float64(5), int64(1), object(7)\n",
+      "memory usage: 55.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print a summary of df_activities using the info() method.\n",
+    "df_activities.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols_to_drop = [\n",
+    "    \"Route Name\",\n",
+    "    \"Friend's Tagged\",\n",
+    "    \"Notes\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Activity Id</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Distance (km)</th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Average Pace</th>\n",
+       "      <th>Average Speed (km/h)</th>\n",
+       "      <th>Calories Burned</th>\n",
+       "      <th>Climb (m)</th>\n",
+       "      <th>Average Heart Rate (bpm)</th>\n",
+       "      <th>GPX File</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-11-11 14:05:12</th>\n",
+       "      <td>c9627fed-14ac-47a2-bed3-2a2630c63c15</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>10.44</td>\n",
+       "      <td>58:40</td>\n",
+       "      <td>5:37</td>\n",
+       "      <td>10.68</td>\n",
+       "      <td>774.0</td>\n",
+       "      <td>130</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>2018-11-11-140512.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-09 15:02:35</th>\n",
+       "      <td>be65818d-a801-4847-a43b-2acdf4dc70e7</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>12.84</td>\n",
+       "      <td>1:14:12</td>\n",
+       "      <td>5:47</td>\n",
+       "      <td>10.39</td>\n",
+       "      <td>954.0</td>\n",
+       "      <td>168</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>2018-11-09-150235.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-04 16:05:00</th>\n",
+       "      <td>c09b2f92-f855-497c-b624-c196b3ef036c</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>13.01</td>\n",
+       "      <td>1:15:16</td>\n",
+       "      <td>5:47</td>\n",
+       "      <td>10.37</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>171</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>2018-11-04-160500.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-01 14:03:58</th>\n",
+       "      <td>bc9b612d-3499-43ff-b82a-9b17b71b8a36</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>12.98</td>\n",
+       "      <td>1:14:25</td>\n",
+       "      <td>5:44</td>\n",
+       "      <td>10.47</td>\n",
+       "      <td>960.0</td>\n",
+       "      <td>169</td>\n",
+       "      <td>158.0</td>\n",
+       "      <td>2018-11-01-140358.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-27 17:01:36</th>\n",
+       "      <td>972567b2-1b0e-437c-9e82-fef8078d6438</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>13.02</td>\n",
+       "      <td>1:12:50</td>\n",
+       "      <td>5:36</td>\n",
+       "      <td>10.73</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>170</td>\n",
+       "      <td>154.0</td>\n",
+       "      <td>2018-10-27-170136.gpx</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Activity Id     Type  \\\n",
+       "Date                                                                 \n",
+       "2018-11-11 14:05:12  c9627fed-14ac-47a2-bed3-2a2630c63c15  Running   \n",
+       "2018-11-09 15:02:35  be65818d-a801-4847-a43b-2acdf4dc70e7  Running   \n",
+       "2018-11-04 16:05:00  c09b2f92-f855-497c-b624-c196b3ef036c  Running   \n",
+       "2018-11-01 14:03:58  bc9b612d-3499-43ff-b82a-9b17b71b8a36  Running   \n",
+       "2018-10-27 17:01:36  972567b2-1b0e-437c-9e82-fef8078d6438  Running   \n",
+       "\n",
+       "                     Distance (km) Duration Average Pace  \\\n",
+       "Date                                                       \n",
+       "2018-11-11 14:05:12          10.44    58:40         5:37   \n",
+       "2018-11-09 15:02:35          12.84  1:14:12         5:47   \n",
+       "2018-11-04 16:05:00          13.01  1:15:16         5:47   \n",
+       "2018-11-01 14:03:58          12.98  1:14:25         5:44   \n",
+       "2018-10-27 17:01:36          13.02  1:12:50         5:36   \n",
+       "\n",
+       "                     Average Speed (km/h)  Calories Burned  Climb (m)  \\\n",
+       "Date                                                                    \n",
+       "2018-11-11 14:05:12                 10.68            774.0        130   \n",
+       "2018-11-09 15:02:35                 10.39            954.0        168   \n",
+       "2018-11-04 16:05:00                 10.37            967.0        171   \n",
+       "2018-11-01 14:03:58                 10.47            960.0        169   \n",
+       "2018-10-27 17:01:36                 10.73            967.0        170   \n",
+       "\n",
+       "                     Average Heart Rate (bpm)               GPX File  \n",
+       "Date                                                                  \n",
+       "2018-11-11 14:05:12                     159.0  2018-11-11-140512.gpx  \n",
+       "2018-11-09 15:02:35                     159.0  2018-11-09-150235.gpx  \n",
+       "2018-11-04 16:05:00                     155.0  2018-11-04-160500.gpx  \n",
+       "2018-11-01 14:03:58                     158.0  2018-11-01-140358.gpx  \n",
+       "2018-10-27 17:01:36                     154.0  2018-10-27-170136.gpx  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_activities.drop(cols_to_drop,axis=1,inplace =True)\n",
+    "df_activities.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Activity Id', 'Type', 'Distance (km)', 'Duration', 'Average Pace',\n",
+       "       'Average Speed (km/h)', 'Calories Burned', 'Climb (m)',\n",
+       "       'Average Heart Rate (bpm)', 'GPX File'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_activities.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "DatetimeIndex: 508 entries, 2018-11-11 14:05:12 to 2012-08-22 18:53:54\n",
+      "Data columns (total 10 columns):\n",
+      "Activity Id                 508 non-null object\n",
+      "Type                        508 non-null object\n",
+      "Distance (km)               508 non-null float64\n",
+      "Duration                    508 non-null object\n",
+      "Average Pace                508 non-null object\n",
+      "Average Speed (km/h)        508 non-null float64\n",
+      "Calories Burned             508 non-null float64\n",
+      "Climb (m)                   508 non-null int64\n",
+      "Average Heart Rate (bpm)    294 non-null float64\n",
+      "GPX File                    504 non-null object\n",
+      "dtypes: float64(4), int64(1), object(5)\n",
+      "memory usage: 43.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_activities.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Running    459\n",
+       "Cycling     29\n",
+       "Walking     18\n",
+       "Other        2\n",
+       "Name: Type, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_activities['Type'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Activity Id</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Distance (km)</th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Average Pace</th>\n",
+       "      <th>Average Speed (km/h)</th>\n",
+       "      <th>Calories Burned</th>\n",
+       "      <th>Climb (m)</th>\n",
+       "      <th>Average Heart Rate (bpm)</th>\n",
+       "      <th>GPX File</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-11-11 14:05:12</th>\n",
+       "      <td>c9627fed-14ac-47a2-bed3-2a2630c63c15</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>10.44</td>\n",
+       "      <td>58:40</td>\n",
+       "      <td>5:37</td>\n",
+       "      <td>10.68</td>\n",
+       "      <td>774.0</td>\n",
+       "      <td>130</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>2018-11-11-140512.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-09 15:02:35</th>\n",
+       "      <td>be65818d-a801-4847-a43b-2acdf4dc70e7</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>12.84</td>\n",
+       "      <td>1:14:12</td>\n",
+       "      <td>5:47</td>\n",
+       "      <td>10.39</td>\n",
+       "      <td>954.0</td>\n",
+       "      <td>168</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>2018-11-09-150235.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-04 16:05:00</th>\n",
+       "      <td>c09b2f92-f855-497c-b624-c196b3ef036c</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>13.01</td>\n",
+       "      <td>1:15:16</td>\n",
+       "      <td>5:47</td>\n",
+       "      <td>10.37</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>171</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>2018-11-04-160500.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-01 14:03:58</th>\n",
+       "      <td>bc9b612d-3499-43ff-b82a-9b17b71b8a36</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>12.98</td>\n",
+       "      <td>1:14:25</td>\n",
+       "      <td>5:44</td>\n",
+       "      <td>10.47</td>\n",
+       "      <td>960.0</td>\n",
+       "      <td>169</td>\n",
+       "      <td>158.0</td>\n",
+       "      <td>2018-11-01-140358.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-27 17:01:36</th>\n",
+       "      <td>972567b2-1b0e-437c-9e82-fef8078d6438</td>\n",
+       "      <td>Running</td>\n",
+       "      <td>13.02</td>\n",
+       "      <td>1:12:50</td>\n",
+       "      <td>5:36</td>\n",
+       "      <td>10.73</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>170</td>\n",
+       "      <td>154.0</td>\n",
+       "      <td>2018-10-27-170136.gpx</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Activity Id     Type  \\\n",
+       "Date                                                                 \n",
+       "2018-11-11 14:05:12  c9627fed-14ac-47a2-bed3-2a2630c63c15  Running   \n",
+       "2018-11-09 15:02:35  be65818d-a801-4847-a43b-2acdf4dc70e7  Running   \n",
+       "2018-11-04 16:05:00  c09b2f92-f855-497c-b624-c196b3ef036c  Running   \n",
+       "2018-11-01 14:03:58  bc9b612d-3499-43ff-b82a-9b17b71b8a36  Running   \n",
+       "2018-10-27 17:01:36  972567b2-1b0e-437c-9e82-fef8078d6438  Running   \n",
+       "\n",
+       "                     Distance (km) Duration Average Pace  \\\n",
+       "Date                                                       \n",
+       "2018-11-11 14:05:12          10.44    58:40         5:37   \n",
+       "2018-11-09 15:02:35          12.84  1:14:12         5:47   \n",
+       "2018-11-04 16:05:00          13.01  1:15:16         5:47   \n",
+       "2018-11-01 14:03:58          12.98  1:14:25         5:44   \n",
+       "2018-10-27 17:01:36          13.02  1:12:50         5:36   \n",
+       "\n",
+       "                     Average Speed (km/h)  Calories Burned  Climb (m)  \\\n",
+       "Date                                                                    \n",
+       "2018-11-11 14:05:12                 10.68            774.0        130   \n",
+       "2018-11-09 15:02:35                 10.39            954.0        168   \n",
+       "2018-11-04 16:05:00                 10.37            967.0        171   \n",
+       "2018-11-01 14:03:58                 10.47            960.0        169   \n",
+       "2018-10-27 17:01:36                 10.73            967.0        170   \n",
+       "\n",
+       "                     Average Heart Rate (bpm)               GPX File  \n",
+       "Date                                                                  \n",
+       "2018-11-11 14:05:12                     159.0  2018-11-11-140512.gpx  \n",
+       "2018-11-09 15:02:35                     159.0  2018-11-09-150235.gpx  \n",
+       "2018-11-04 16:05:00                     155.0  2018-11-04-160500.gpx  \n",
+       "2018-11-01 14:03:58                     158.0  2018-11-01-140358.gpx  \n",
+       "2018-10-27 17:01:36                     154.0  2018-10-27-170136.gpx  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_activities['Type'] = df_activities['Type'].str.replace('Other','Unicycling')\n",
+    "df_activities.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Activity Id                   0\n",
+       "Type                          0\n",
+       "Distance (km)                 0\n",
+       "Duration                      0\n",
+       "Average Pace                  0\n",
+       "Average Speed (km/h)          0\n",
+       "Calories Burned               0\n",
+       "Climb (m)                     0\n",
+       "Average Heart Rate (bpm)    214\n",
+       "GPX File                      4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_activities.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "124.4"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Task 3\n",
+    "avg_hr_cycle = df_activities[df_activities['Type']== 'Cycling']['Average Heart Rate (bpm)'].mean()\n",
+    "avg_hr_cycle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Activity Id</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Distance (km)</th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Average Pace</th>\n",
+       "      <th>Average Speed (km/h)</th>\n",
+       "      <th>Calories Burned</th>\n",
+       "      <th>Climb (m)</th>\n",
+       "      <th>Average Heart Rate (bpm)</th>\n",
+       "      <th>GPX File</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-10-06 16:45:02</th>\n",
+       "      <td>4c163abe-3a57-42fd-b50b-7f365960cbd4</td>\n",
+       "      <td>Cycling</td>\n",
+       "      <td>19.63</td>\n",
+       "      <td>1:26:26</td>\n",
+       "      <td>4:24</td>\n",
+       "      <td>13.63</td>\n",
+       "      <td>577.0</td>\n",
+       "      <td>210</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>2018-10-06-164502.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-09-16 14:55:03</th>\n",
+       "      <td>30aaa821-1d3a-4f2f-9688-8543cebbd6e8</td>\n",
+       "      <td>Cycling</td>\n",
+       "      <td>32.61</td>\n",
+       "      <td>1:55:15</td>\n",
+       "      <td>3:32</td>\n",
+       "      <td>16.98</td>\n",
+       "      <td>830.0</td>\n",
+       "      <td>462</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2018-09-16-145503.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-09-01 17:06:15</th>\n",
+       "      <td>2bd1841f-b428-4683-a41b-2bfb4be7e908</td>\n",
+       "      <td>Cycling</td>\n",
+       "      <td>36.89</td>\n",
+       "      <td>1:58:39</td>\n",
+       "      <td>3:13</td>\n",
+       "      <td>18.65</td>\n",
+       "      <td>937.0</td>\n",
+       "      <td>491</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>2018-09-01-170615.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-08-28 18:44:33</th>\n",
+       "      <td>c9a8e088-441d-4b3f-bfbc-287e87585ca7</td>\n",
+       "      <td>Cycling</td>\n",
+       "      <td>28.17</td>\n",
+       "      <td>1:27:07</td>\n",
+       "      <td>3:06</td>\n",
+       "      <td>19.40</td>\n",
+       "      <td>685.0</td>\n",
+       "      <td>400</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>2018-08-28-184433.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-08-25 17:18:32</th>\n",
+       "      <td>12723b6e-571b-4b68-be17-2c797982d3f9</td>\n",
+       "      <td>Cycling</td>\n",
+       "      <td>19.41</td>\n",
+       "      <td>1:11:33</td>\n",
+       "      <td>3:41</td>\n",
+       "      <td>16.28</td>\n",
+       "      <td>536.0</td>\n",
+       "      <td>199</td>\n",
+       "      <td>124.0</td>\n",
+       "      <td>2018-08-25-171832.gpx</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Activity Id     Type  \\\n",
+       "Date                                                                 \n",
+       "2018-10-06 16:45:02  4c163abe-3a57-42fd-b50b-7f365960cbd4  Cycling   \n",
+       "2018-09-16 14:55:03  30aaa821-1d3a-4f2f-9688-8543cebbd6e8  Cycling   \n",
+       "2018-09-01 17:06:15  2bd1841f-b428-4683-a41b-2bfb4be7e908  Cycling   \n",
+       "2018-08-28 18:44:33  c9a8e088-441d-4b3f-bfbc-287e87585ca7  Cycling   \n",
+       "2018-08-25 17:18:32  12723b6e-571b-4b68-be17-2c797982d3f9  Cycling   \n",
+       "\n",
+       "                     Distance (km) Duration Average Pace  \\\n",
+       "Date                                                       \n",
+       "2018-10-06 16:45:02          19.63  1:26:26         4:24   \n",
+       "2018-09-16 14:55:03          32.61  1:55:15         3:32   \n",
+       "2018-09-01 17:06:15          36.89  1:58:39         3:13   \n",
+       "2018-08-28 18:44:33          28.17  1:27:07         3:06   \n",
+       "2018-08-25 17:18:32          19.41  1:11:33         3:41   \n",
+       "\n",
+       "                     Average Speed (km/h)  Calories Burned  Climb (m)  \\\n",
+       "Date                                                                    \n",
+       "2018-10-06 16:45:02                 13.63            577.0        210   \n",
+       "2018-09-16 14:55:03                 16.98            830.0        462   \n",
+       "2018-09-01 17:06:15                 18.65            937.0        491   \n",
+       "2018-08-28 18:44:33                 19.40            685.0        400   \n",
+       "2018-08-25 17:18:32                 16.28            536.0        199   \n",
+       "\n",
+       "                     Average Heart Rate (bpm)               GPX File  \n",
+       "Date                                                                  \n",
+       "2018-10-06 16:45:02                      79.0  2018-10-06-164502.gpx  \n",
+       "2018-09-16 14:55:03                     118.0  2018-09-16-145503.gpx  \n",
+       "2018-09-01 17:06:15                     122.0  2018-09-01-170615.gpx  \n",
+       "2018-08-28 18:44:33                     111.0  2018-08-28-184433.gpx  \n",
+       "2018-08-25 17:18:32                     124.0  2018-08-25-171832.gpx  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_cycle = df_activities[df_activities['Type'] == 'Cycling'].copy()\n",
+    "df_cycle.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Activity Id</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Distance (km)</th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Average Pace</th>\n",
+       "      <th>Average Speed (km/h)</th>\n",
+       "      <th>Calories Burned</th>\n",
+       "      <th>Climb (m)</th>\n",
+       "      <th>Average Heart Rate (bpm)</th>\n",
+       "      <th>GPX File</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-10-06 16:45:02</th>\n",
+       "      <td>4c163abe-3a57-42fd-b50b-7f365960cbd4</td>\n",
+       "      <td>Cycling</td>\n",
+       "      <td>19.63</td>\n",
+       "      <td>1:26:26</td>\n",
+       "      <td>4:24</td>\n",
+       "      <td>13.63</td>\n",
+       "      <td>577.0</td>\n",
+       "      <td>210</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>2018-10-06-164502.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-09-16 14:55:03</th>\n",
+       "      <td>30aaa821-1d3a-4f2f-9688-8543cebbd6e8</td>\n",
+       "      <td>Cycling</td>\n",
+       "      <td>32.61</td>\n",
+       "      <td>1:55:15</td>\n",
+       "      <td>3:32</td>\n",
+       "      <td>16.98</td>\n",
+       "      <td>830.0</td>\n",
+       "      <td>462</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2018-09-16-145503.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-09-01 17:06:15</th>\n",
+       "      <td>2bd1841f-b428-4683-a41b-2bfb4be7e908</td>\n",
+       "      <td>Cycling</td>\n",
+       "      <td>36.89</td>\n",
+       "      <td>1:58:39</td>\n",
+       "      <td>3:13</td>\n",
+       "      <td>18.65</td>\n",
+       "      <td>937.0</td>\n",
+       "      <td>491</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>2018-09-01-170615.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-08-28 18:44:33</th>\n",
+       "      <td>c9a8e088-441d-4b3f-bfbc-287e87585ca7</td>\n",
+       "      <td>Cycling</td>\n",
+       "      <td>28.17</td>\n",
+       "      <td>1:27:07</td>\n",
+       "      <td>3:06</td>\n",
+       "      <td>19.40</td>\n",
+       "      <td>685.0</td>\n",
+       "      <td>400</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>2018-08-28-184433.gpx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-08-25 17:18:32</th>\n",
+       "      <td>12723b6e-571b-4b68-be17-2c797982d3f9</td>\n",
+       "      <td>Cycling</td>\n",
+       "      <td>19.41</td>\n",
+       "      <td>1:11:33</td>\n",
+       "      <td>3:41</td>\n",
+       "      <td>16.28</td>\n",
+       "      <td>536.0</td>\n",
+       "      <td>199</td>\n",
+       "      <td>124.0</td>\n",
+       "      <td>2018-08-25-171832.gpx</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Activity Id     Type  \\\n",
+       "Date                                                                 \n",
+       "2018-10-06 16:45:02  4c163abe-3a57-42fd-b50b-7f365960cbd4  Cycling   \n",
+       "2018-09-16 14:55:03  30aaa821-1d3a-4f2f-9688-8543cebbd6e8  Cycling   \n",
+       "2018-09-01 17:06:15  2bd1841f-b428-4683-a41b-2bfb4be7e908  Cycling   \n",
+       "2018-08-28 18:44:33  c9a8e088-441d-4b3f-bfbc-287e87585ca7  Cycling   \n",
+       "2018-08-25 17:18:32  12723b6e-571b-4b68-be17-2c797982d3f9  Cycling   \n",
+       "\n",
+       "                     Distance (km) Duration Average Pace  \\\n",
+       "Date                                                       \n",
+       "2018-10-06 16:45:02          19.63  1:26:26         4:24   \n",
+       "2018-09-16 14:55:03          32.61  1:55:15         3:32   \n",
+       "2018-09-01 17:06:15          36.89  1:58:39         3:13   \n",
+       "2018-08-28 18:44:33          28.17  1:27:07         3:06   \n",
+       "2018-08-25 17:18:32          19.41  1:11:33         3:41   \n",
+       "\n",
+       "                     Average Speed (km/h)  Calories Burned  Climb (m)  \\\n",
+       "Date                                                                    \n",
+       "2018-10-06 16:45:02                 13.63            577.0        210   \n",
+       "2018-09-16 14:55:03                 16.98            830.0        462   \n",
+       "2018-09-01 17:06:15                 18.65            937.0        491   \n",
+       "2018-08-28 18:44:33                 19.40            685.0        400   \n",
+       "2018-08-25 17:18:32                 16.28            536.0        199   \n",
+       "\n",
+       "                     Average Heart Rate (bpm)               GPX File  \n",
+       "Date                                                                  \n",
+       "2018-10-06 16:45:02                      79.0  2018-10-06-164502.gpx  \n",
+       "2018-09-16 14:55:03                     118.0  2018-09-16-145503.gpx  \n",
+       "2018-09-01 17:06:15                     122.0  2018-09-01-170615.gpx  \n",
+       "2018-08-28 18:44:33                     111.0  2018-08-28-184433.gpx  \n",
+       "2018-08-25 17:18:32                     124.0  2018-08-25-171832.gpx  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_cycle['Average Heart Rate (bpm)'].fillna(value = int(avg_hr_cycle),inplace = True)\n",
+    "df_cycle.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Activity Id                 0\n",
+       "Type                        0\n",
+       "Distance (km)               0\n",
+       "Duration                    0\n",
+       "Average Pace                0\n",
+       "Average Speed (km/h)        0\n",
+       "Calories Burned             0\n",
+       "Climb (m)                   0\n",
+       "Average Heart Rate (bpm)    0\n",
+       "GPX File                    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_cycle.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_rn_cycle = df_activities[df_activities['Type']== 'Running']['Average Heart Rate (bpm)'].mean()\n",
+    "df_run=df_activities[df_activities['Type']== 'Running'].copy()\n",
+    "df_run['Average Heart Rate (bpm)'].fillna(value=int(avg_rn_cycle), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001F6159317B8>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001F615B1BB00>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001F6160C0080>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001F6160E8710>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001F61610FDA0>],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Task 4\n",
+    "import matplotlib.pyplot as plt\n",
+    "runs_subset_2013_2018 = df_run.loc[\"20190101\":\"20130101\"]\n",
+    "\n",
+    "# plotting\n",
+    "runs_subset_2013_2018.plot(subplots=True, sharex=False,figsize=(12,12), marker=\"o\", markersize=3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Avg of my annual stats of 4 years\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Distance (km)</th>\n",
+       "      <th>Average Speed (km/h)</th>\n",
+       "      <th>Calories Burned</th>\n",
+       "      <th>Climb (m)</th>\n",
+       "      <th>Average Heart Rate (bpm)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>13.602805</td>\n",
+       "      <td>10.998902</td>\n",
+       "      <td>932.906138</td>\n",
+       "      <td>160.170732</td>\n",
+       "      <td>143.353659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>11.411667</td>\n",
+       "      <td>10.837778</td>\n",
+       "      <td>796.152777</td>\n",
+       "      <td>133.194444</td>\n",
+       "      <td>143.388889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-31</th>\n",
+       "      <td>12.935176</td>\n",
+       "      <td>10.959059</td>\n",
+       "      <td>914.164706</td>\n",
+       "      <td>169.376471</td>\n",
+       "      <td>145.247059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31</th>\n",
+       "      <td>13.339063</td>\n",
+       "      <td>10.777969</td>\n",
+       "      <td>952.359375</td>\n",
+       "      <td>191.218750</td>\n",
+       "      <td>148.125000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Distance (km)  Average Speed (km/h)  Calories Burned   Climb (m)  \\\n",
+       "Date                                                                           \n",
+       "2015-12-31      13.602805             10.998902       932.906138  160.170732   \n",
+       "2016-12-31      11.411667             10.837778       796.152777  133.194444   \n",
+       "2017-12-31      12.935176             10.959059       914.164706  169.376471   \n",
+       "2018-12-31      13.339063             10.777969       952.359375  191.218750   \n",
+       "\n",
+       "            Average Heart Rate (bpm)  \n",
+       "Date                                  \n",
+       "2015-12-31                143.353659  \n",
+       "2016-12-31                143.388889  \n",
+       "2017-12-31                145.247059  \n",
+       "2018-12-31                148.125000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Task 5\n",
+    "runs_subset_2015_2018=df_run.loc['20190101':'20150101']\n",
+    "#Counting the annual averages using 'A' alias\n",
+    "print(\"Avg of my annual stats of 4 years\")\n",
+    "display(runs_subset_2015_2018.resample('A').mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Avg of my Weekly stats of 4 years\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Distance (km)                12.518176\n",
+       "Average Speed (km/h)         10.835473\n",
+       "Calories Burned             877.861969\n",
+       "Climb (m)                   158.325444\n",
+       "Average Heart Rate (bpm)    144.801775\n",
+       "dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Counting the weekly averages using 'W' alias\n",
+    "print(\"Avg of my Weekly stats of 4 years\")\n",
+    "display(runs_subset_2015_2018.resample('W').mean().mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "weekly_counts_average=runs_subset_2015_2018['Distance (km)'].resample('W').count().mean()\n",
+    "print(weekly_counts_average)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Task6\n",
+    "runs_distance=runs_subset_2015_2018['Distance (km)']\n",
+    "runs_hr=runs_subset_2015_2018['Average Heart Rate (bpm)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x1f616896588>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,(ax1,ax2)=plt.subplots(2,sharex=True,figsize=(12,8))\n",
+    "#plotting the first subplot.\n",
+    "ax1=runs_distance.plot(ax=ax1)\n",
+    "ax1.set(ylabel='Distace(KM)')\n",
+    "#plotting the second subplot.\n",
+    "runs_hr.plot(ax=ax2)\n",
+    "ax2.set(xlabel='Date')\n",
+    "ax2.axhline(runs_hr.mean(),color=\"blue\",linewidth=1,linestyle='-.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "459\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Task7\n",
+    "df_run_dist_annual=df_run[\"Distance (km)\"].resample(\"A\").count()\n",
+    "print(df_run_dist_annual.sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Polygon at 0x1f61692d908>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig=plt.figure(figsize=(8,5))\n",
+    "ax=df_run_dist_annual.plot(marker=\"*\", markersize=20,linewidth=0, color=\"blue\")\n",
+    "ax.set(ylim=[0,800],xlim=[\"2012\",\"2019\"],ylabel=\"Distance(km)\")\n",
+    "ax.axhspan(0,800,color=\"red\",alpha=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Task 8\n",
+    "import statsmodels.api as sm\n",
+    "#Filling NaN values in Distance(km) col.\n",
+    "df_run_dist_wkly=df_run[\"Distance (km)\"].resample('W').bfill()\n",
+    "fig=plt.figure(figsize=(12,5))\n",
+    "ax=df_run_dist_wkly.plot(label=\"Trend\", linewidth=2)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Task 9\n",
+    "df_run_hr_all=df_run.loc[\"20190101\":\"20150101\"][\"Average Heart Rate (bpm)\"]\n",
+    "fig,ax=plt.subplots(figsize=(8,5))\n",
+    "zone_names = [\"Easy\", \"Moderate\", \"hard\", \"Very Hard\", \"maximal\",\"Extreme\"]\n",
+    "hr_zones = [100,125,133,142,151,173]\n",
+    "ax.hist(df_run_hr_all,bins=hr_zones)\n",
+    "ax.set_xticklabels(labels=zone_names, rotation=-30, ha=\"left\")\n",
+    "ax.xaxis.set(ticks=hr_zones)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Task 10\n",
+    "df_walk = df_activities[df_activities[\"Type\"]==\"Walking\"].copy()\n",
+    "df_walk[\"Average Heart Rate (bpm)\"].fillna(110,inplace=True)\n",
+    "\n",
+    "# concatenating the df_run, df_walk & df_cycle DataFrame\n",
+    "df_run_walk_cycle = df_run.append([df_walk], [df_cycle]).sort_index(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         Distance (km)  Climb (m)\n",
+      "Type                             \n",
+      "Running        5224.50      57278\n",
+      "Walking          33.45        349\n"
+     ]
+    }
+   ],
+   "source": [
+    "dist_climb_cols, speed_col = [\"Distance (km)\",\"Climb (m)\"],[\"Average Heart Rate (bpm)\"]\n",
+    "\n",
+    "# calcultating total distance and climb in each activity type\n",
+    "df_totals = df_run_walk_cycle.groupby(\"Type\")[dist_climb_cols].sum()\n",
+    "print(df_totals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Statistics\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Average Heart Rate (bpm)</th>\n",
+       "      <th>Climb (m)</th>\n",
+       "      <th>Distance (km)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"9\" valign=\"top\">Running</th>\n",
+       "      <th>25%</th>\n",
+       "      <td>143.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>7.415000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>144.000000</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>10.810000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>146.000000</td>\n",
+       "      <td>171.000000</td>\n",
+       "      <td>13.190000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>459.000000</td>\n",
+       "      <td>459.000000</td>\n",
+       "      <td>459.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>172.000000</td>\n",
+       "      <td>982.000000</td>\n",
+       "      <td>38.320000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>144.594771</td>\n",
+       "      <td>124.788671</td>\n",
+       "      <td>11.382353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>113.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.760000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>6.069939</td>\n",
+       "      <td>103.382177</td>\n",
+       "      <td>4.937853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>57278.000000</td>\n",
+       "      <td>5224.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"9\" valign=\"top\">Walking</th>\n",
+       "      <th>25%</th>\n",
+       "      <td>110.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>1.385000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>110.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>1.485000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>110.000000</td>\n",
+       "      <td>15.500000</td>\n",
+       "      <td>1.787500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>110.000000</td>\n",
+       "      <td>112.000000</td>\n",
+       "      <td>4.290000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>110.000000</td>\n",
+       "      <td>19.388889</td>\n",
+       "      <td>1.858333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>110.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.220000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>27.110100</td>\n",
+       "      <td>0.880055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>349.000000</td>\n",
+       "      <td>33.450000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Average Heart Rate (bpm)     Climb (m)  Distance (km)\n",
+       "Type                                                                \n",
+       "Running 25%                  143.000000     54.000000       7.415000\n",
+       "        50%                  144.000000     91.000000      10.810000\n",
+       "        75%                  146.000000    171.000000      13.190000\n",
+       "        count                459.000000    459.000000     459.000000\n",
+       "        max                  172.000000    982.000000      38.320000\n",
+       "        mean                 144.594771    124.788671      11.382353\n",
+       "        min                  113.000000      0.000000       0.760000\n",
+       "        std                    6.069939    103.382177       4.937853\n",
+       "        total                       NaN  57278.000000    5224.500000\n",
+       "Walking 25%                  110.000000      7.000000       1.385000\n",
+       "        50%                  110.000000     10.000000       1.485000\n",
+       "        75%                  110.000000     15.500000       1.787500\n",
+       "        count                 18.000000     18.000000      18.000000\n",
+       "        max                  110.000000    112.000000       4.290000\n",
+       "        mean                 110.000000     19.388889       1.858333\n",
+       "        min                  110.000000      5.000000       1.220000\n",
+       "        std                    0.000000     27.110100       0.880055\n",
+       "        total                       NaN    349.000000      33.450000"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# full summary report\n",
+    "df_summary = df_run_walk_cycle.groupby(\"Type\")[dist_climb_cols + speed_col].describe()\n",
+    "\n",
+    "# total summary \n",
+    "for i in dist_climb_cols:\n",
+    "  df_summary[i, \"total\"] = df_totals[i]\n",
+    "print(\"Total Statistics\")\n",
+    "df_summary.stack()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "33.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Task11\n",
+    "# average shoes for lifetime\n",
+    "average_shoes_lifetime = 5224/7\n",
+    "\n",
+    "#number of shoes for forest run\n",
+    "shoes_for_forrest_run = 24700//average_shoes_lifetime\n",
+    "print(shoes_for_forrest_run)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Intermediate/Data Analysis/dataset/cardioActivities.csv b/Intermediate/Data Analysis/dataset/cardioActivities.csv
new file mode 100644
index 0000000..4d48eee
--- /dev/null
+++ b/Intermediate/Data Analysis/dataset/cardioActivities.csv	
@@ -0,0 +1,511 @@
+Date,Activity Id,Type,Route Name,Distance (km),Duration,Average Pace,Average Speed (km/h),Calories Burned,Climb (m),Average Heart Rate (bpm),Friend's Tagged,Notes,GPX File
+2018-11-11 14:05:12,c9627fed-14ac-47a2-bed3-2a2630c63c15,Running,,10.44,58:40,5:37,10.68,774.0,130,159.0,,,2018-11-11-140512.gpx
+2018-11-09 15:02:35,be65818d-a801-4847-a43b-2acdf4dc70e7,Running,,12.84,1:14:12,5:47,10.39,954.0,168,159.0,,,2018-11-09-150235.gpx
+2018-11-04 16:05:00,c09b2f92-f855-497c-b624-c196b3ef036c,Running,,13.01,1:15:16,5:47,10.37,967.0,171,155.0,,,2018-11-04-160500.gpx
+2018-11-01 14:03:58,bc9b612d-3499-43ff-b82a-9b17b71b8a36,Running,,12.98,1:14:25,5:44,10.47,960.0,169,158.0,,,2018-11-01-140358.gpx
+2018-10-27 17:01:36,972567b2-1b0e-437c-9e82-fef8078d6438,Running,,13.02,1:12:50,5:36,10.73,967.0,170,154.0,,,2018-10-27-170136.gpx
+2018-10-19 17:52:32,fe2cb3fc-6330-40fa-8a92-0f86f4e72282,Running,,10.29,59:18,5:46,10.41,764.0,133,155.0,,,2018-10-19-175232.gpx
+2018-10-14 17:28:56,96acedc9-d3d5-4aac-8df4-f549a6418c1d,Running,,12.93,1:10:16,5:26,11.04,953.0,159,158.0,,,2018-10-14-172856.gpx
+2018-10-12 17:41:58,3c91092b-e6f3-4565-b540-6b6537358006,Running,,12.31,1:09:26,5:38,10.64,903.0,134,157.0,,,2018-10-12-174158.gpx
+2018-10-06 16:45:02,4c163abe-3a57-42fd-b50b-7f365960cbd4,Cycling,,19.63,1:26:26,4:24,13.63,577.0,210,79.0,,,2018-10-06-164502.gpx
+2018-09-30 16:52:34,70c7f2ba-f4cb-4403-a9e4-bf2c9b93bb05,Running,,12.97,1:13:56,5:42,10.52,964.0,171,156.0,,,2018-09-30-165234.gpx
+2018-09-16 14:55:03,30aaa821-1d3a-4f2f-9688-8543cebbd6e8,Cycling,,32.61,1:55:15,3:32,16.98,830.0,462,118.0,,,2018-09-16-145503.gpx
+2018-09-02 17:24:28,51781e97-560e-48d0-9522-44f303f9235f,Other,,17.65,1:01:28,3:29,17.23,1164.0,219,77.0,,,2018-09-02-172428.gpx
+2018-09-01 17:06:15,2bd1841f-b428-4683-a41b-2bfb4be7e908,Cycling,,36.89,1:58:39,3:13,18.65,937.0,491,122.0,,,2018-09-01-170615.gpx
+2018-08-28 18:44:33,c9a8e088-441d-4b3f-bfbc-287e87585ca7,Cycling,,28.17,1:27:07,3:06,19.4,685.0,400,111.0,,,2018-08-28-184433.gpx
+2018-08-25 17:18:32,12723b6e-571b-4b68-be17-2c797982d3f9,Cycling,,19.41,1:11:33,3:41,16.28,536.0,199,124.0,,,2018-08-25-171832.gpx
+2018-08-22 18:25:22,503a12b4-9a16-4c63-bbcd-be528ae0787a,Running,,4.38,33:55,7:45,7.75,334.0,60,138.0,,,2018-08-22-182522.gpx
+2018-08-12 17:37:17,fa9f57d9-4995-4efe-9eb8-00983fde4486,Running,,22.09,2:13:22,6:02,9.94,1652.0,342,148.0,,,2018-08-12-173717.gpx
+2018-08-08 18:44:59,dc1669de-2d66-4e14-a24f-ba0ed06d70a1,Running,,15.27,1:19:37,5:13,11.51,1143.0,241,150.0,,,2018-08-08-184459.gpx
+2018-08-05 10:17:10,d4a6b5b2-2dba-464a-a26e-eec0b67db077,Running,,18.7,1:49:19,5:51,10.26,1397.0,280,150.0,,,2018-08-05-101710.gpx
+2018-08-01 18:20:26,03006a09-870b-4f15-bb6c-9b979793e75a,Running,,18.2,1:43:05,5:40,10.59,1355.0,259,145.0,,,2018-08-01-182026.gpx
+2018-07-31 18:25:24,a929c543-bc2e-446d-9431-40e60fbc6270,Other,,16.8,1:07:21,4:01,14.97,1075.0,202,94.0,,,2018-07-31-182524.gpx
+2018-07-30 18:18:47,6c6dd4aa-7567-40c7-8a8c-e6fc33cc80fb,Running,,17.87,1:44:00,5:49,10.31,1269.0,261,141.0,,TomTom MySports Watch,2018-07-30-181847.gpx
+2018-07-27 11:43:03,eb6a80cb-bf87-4426-bc11-9610b7b70cba,Running,,12.71,1:14:51,5:53,10.19,893.0,180,145.0,,TomTom MySports Watch,2018-07-27-114303.gpx
+2018-07-20 08:01:51,83e5d964-2630-475d-9c17-d906ddd59eb0,Running,,11.01,59:18,5:23,11.14,795.0,85,,,,2018-07-20-080151.gpx
+2018-07-18 08:08:39,1906f2c4-e77e-4364-98f2-f4eb1d8c9960,Running,,11.04,55:41,5:03,11.9,801.0,87,,,,2018-07-18-080839.gpx
+2018-07-13 17:43:20,324c89a8-c3c9-4576-867e-9038ba3fe761,Running,,17.96,1:41:36,5:40,10.6,1271.0,258,145.0,,TomTom MySports Watch,2018-07-13-174320.gpx
+2018-07-07 16:02:30,4968a9c1-bc7a-4568-8394-815a406a6c86,Running,,17.85,1:37:40,5:28,10.97,1261.0,266,149.0,,TomTom MySports Watch,2018-07-07-160230.gpx
+2018-07-03 18:00:05,e91eba7e-dd4c-4ae4-8909-c35dc6e2debb,Running,,18.75,1:41:30,5:25,11.08,1332.0,300,139.0,,TomTom MySports Watch,2018-07-03-180005.gpx
+2018-06-28 12:24:25,8aa44a93-bf2a-449b-bee4-603135afec7b,Running,,17.95,1:36:59,5:24,11.1,1261.0,260,148.0,,TomTom MySports Watch,2018-06-28-122425.gpx
+2018-06-25 18:11:12,4007c802-acff-47ad-a9b0-8403ef8d3def,Running,,18.72,1:39:27,5:19,11.3,1317.0,301,142.0,,TomTom MySports Watch,2018-06-25-181112.gpx
+2018-06-21 18:03:18,71c6e320-9ca9-4504-9cd6-4e8636ce5018,Running,,14.38,1:15:59,5:17,11.36,1007.0,211,145.0,,TomTom MySports Watch,2018-06-21-180318.gpx
+2018-06-18 18:17:49,874c8870-8621-4356-81fd-e309d38f714a,Running,,11.63,1:02:52,5:24,11.1,811.0,145,141.0,,TomTom MySports Watch,2018-06-18-181749.gpx
+2018-06-16 17:29:00,b5c6d02f-8c7f-4359-8263-29a9ca658af1,Running,,14.95,1:19:03,5:17,11.35,1057.0,243,147.0,,TomTom MySports Watch,2018-06-16-172900.gpx
+2018-06-13 18:50:49,2f10b609-c2ef-4d8d-90ad-bd49cec2c41b,Running,,12.75,1:07:30,5:18,11.34,890.0,173,147.0,,TomTom MySports Watch,2018-06-13-185049.gpx
+2018-06-11 18:23:11,784ecb1a-3614-47e1-a0ba-651f29c98f4c,Running,,15.74,1:22:58,5:16,11.38,1101.0,209,147.0,,TomTom MySports Watch,2018-06-11-182311.gpx
+2018-06-06 18:44:29,e65e79d8-d30e-4ec1-b15e-aa7c63385dc9,Running,,12.78,1:07:56,5:19,11.29,901.0,170,143.0,,TomTom MySports Watch,2018-06-06-184429.gpx
+2018-06-03 17:21:51,7f80d0e7-d2b2-455a-99fd-baa10dabc2b7,Running,,17.58,1:33:29,5:19,11.28,1240.0,235,149.0,,TomTom MySports Watch,2018-06-03-172151.gpx
+2018-05-30 18:48:00,568802c2-29e3-481d-abc6-a504ee825345,Running,,12.81,1:08:12,5:20,11.27,907.0,172,147.0,,TomTom MySports Watch,2018-05-30-184800.gpx
+2018-05-27 17:31:09,14196951-8a86-40f1-a99b-a966d26bb616,Running,,21.73,2:03:22,5:41,10.57,1528.0,343,140.0,,TomTom MySports Watch,2018-05-27-173109.gpx
+2018-05-23 18:13:17,75a8e0c9-98f5-4f1c-a72f-31eab419477d,Running,,15.59,1:20:32,5:10,11.62,1078.0,208,158.0,,TomTom MySports Watch,2018-05-23-181317.gpx
+2018-05-21 18:29:35,5527e00c-758e-411c-87ae-21669adcbc7c,Running,,12.79,1:07:03,5:15,11.44,899.0,168,144.0,,TomTom MySports Watch,2018-05-21-182935.gpx
+2018-05-18 18:38:50,bfe5cada-e8c9-4e01-8a99-6baa5190d902,Running,,12.79,1:05:52,5:09,11.66,893.0,168,147.0,,TomTom MySports Watch,2018-05-18-183850.gpx
+2018-05-15 18:56:19,775b794a-fde4-485c-a200-112a5d325a27,Running,,12.8,1:05:52,5:09,11.66,889.0,167,148.0,,TomTom MySports Watch,2018-05-15-185619.gpx
+2018-05-09 18:03:20,90b07c52-c7e6-4458-a73f-7278beedde39,Running,,12.76,1:08:31,5:22,11.18,892.0,170,143.0,,TomTom MySports Watch,2018-05-09-180320.gpx
+2018-05-05 11:43:10,daf91acd-00ea-4e13-b49b-11aba2980590,Running,,15.38,1:30:56,5:55,10.15,1098.0,234,152.0,,TomTom MySports Watch,2018-05-05-114310.gpx
+2018-05-02 18:22:09,f99fd0ac-163d-4752-ba14-28f9f4361a63,Running,,12.77,1:07:59,5:19,11.27,894.0,174,147.0,,TomTom MySports Watch,2018-05-02-182209.gpx
+2018-04-29 16:53:30,58da30d2-6e3c-44d2-849e-6530743419e4,Running,,15.0,1:25:03,5:40,10.58,1061.0,245,149.0,,TomTom MySports Watch,2018-04-29-165330.gpx
+2018-04-24 18:17:23,0e04a385-bc28-416d-b173-d37feb3df7c7,Running,,12.84,1:08:48,5:22,11.2,902.0,171,143.0,,TomTom MySports Watch,2018-04-24-181723.gpx
+2018-04-21 18:18:13,e870e465-ae64-405f-b05f-f3d7737b5347,Running,,12.7,1:07:12,5:18,11.34,893.0,171,148.0,,TomTom MySports Watch,2018-04-21-181813.gpx
+2018-04-18 18:16:49,f66c27e3-a1c7-45c2-baa4-d2e583522c18,Running,,12.84,1:06:56,5:13,11.51,901.0,172,146.0,,TomTom MySports Watch,2018-04-18-181649.gpx
+2018-04-15 11:32:26,13b1f4bc-9d04-448b-9b0b-44d340c3b47c,Running,,12.76,1:07:57,5:20,11.26,892.0,169,154.0,,TomTom MySports Watch,2018-04-15-113226.gpx
+2018-04-13 18:37:12,911b312b-cf1d-499d-bb46-cdeb31da9c9e,Running,,12.9,1:07:47,5:15,11.42,908.0,169,147.0,,TomTom MySports Watch,2018-04-13-183712.gpx
+2018-04-11 18:17:35,5581c2b2-e254-46f8-99b0-388913baf5cf,Running,,12.82,1:07:10,5:14,11.45,892.0,173,147.0,,TomTom MySports Watch,2018-04-11-181735.gpx
+2018-04-08 11:41:49,cfddcad3-7551-4944-b218-06b28149ac54,Running,,16.48,1:31:35,5:33,10.8,1158.0,236,164.0,,TomTom MySports Watch,2018-04-08-114149.gpx
+2018-04-06 18:01:30,eaaf724a-d9fd-4937-8aaa-9e6445aaaad5,Running,,12.75,1:10:54,5:34,10.79,900.0,170,154.0,,TomTom MySports Watch,2018-04-06-180130.gpx
+2018-04-03 18:43:05,dff3a1e2-ba08-4694-95dd-00acd3f1bf76,Running,,8.32,46:53,5:38,10.65,582.0,135,143.0,,TomTom MySports Watch,2018-04-03-184305.gpx
+2018-03-26 18:33:12,e92b419b-61ee-45d6-9264-2f47e70f6d85,Running,,11.82,1:05:28,5:32,10.83,841.0,202,150.0,,TomTom MySports Watch,2018-03-26-183312.gpx
+2018-03-21 18:57:05,98b6e3aa-d64a-4aa1-96e2-4b27cc428ba9,Running,,6.4,37:41,5:53,10.19,458.0,116,137.0,,TomTom MySports Watch,2018-03-21-185705.gpx
+2018-03-18 18:14:45,a3e837f5-c99e-4ea1-9e51-b8a9b0a2544f,Running,,10.36,1:00:17,5:49,10.31,738.0,188,147.0,,TomTom MySports Watch,2018-03-18-181445.gpx
+2018-03-15 18:31:35,462e7678-375e-4b97-bef4-95596625b711,Running,,10.15,58:40,5:47,10.38,719.0,182,149.0,,TomTom MySports Watch,2018-03-15-183135.gpx
+2018-02-19 18:28:16,f37df89c-b59a-406a-a509-3ee09f080aa6,Running,,10.42,1:00:10,5:46,10.39,742.0,174,143.0,,TomTom MySports Watch,2018-02-19-182816.gpx
+2018-02-15 19:11:33,dd258b46-8c3b-429a-9884-c38d29f1a77d,Running,,10.27,1:00:19,5:52,10.22,730.0,174,146.0,,TomTom MySports Watch,2018-02-15-191133.gpx
+2018-02-13 18:27:44,cd9359da-2c5b-4c41-ac83-b4c237a85701,Running,,10.6,1:01:20,5:47,10.37,750.0,179,146.0,,TomTom MySports Watch,2018-02-13-182744.gpx
+2018-02-04 17:47:37,e8203d40-8298-4484-9380-617d6b0d3aa5,Running,,10.08,58:57,5:51,10.26,713.0,175,141.0,,TomTom MySports Watch,2018-02-04-174737.gpx
+2018-01-28 13:23:30,d1d36e85-9f28-4ab4-a4c2-60aea8f77490,Running,,13.0,1:13:42,5:40,10.59,910.0,174,152.0,,TomTom MySports Watch,2018-01-28-132330.gpx
+2018-01-24 18:13:26,6d853ab2-e3c3-4206-af59-e22096f1b1d5,Running,,11.59,1:10:30,6:05,9.86,847.0,196,143.0,,TomTom MySports Watch,2018-01-24-181326.gpx
+2018-01-21 11:46:12,2a24b579-7365-49da-a4e4-67c13d097e39,Running,,10.52,1:00:59,5:48,10.35,744.0,153,154.0,,TomTom MySports Watch,2018-01-21-114612.gpx
+2018-01-14 12:48:41,039df468-14d0-4fdd-8496-c9f06f5a5d47,Running,,13.01,1:12:44,5:35,10.73,910.0,180,153.0,,TomTom MySports Watch,2018-01-14-124841.gpx
+2018-01-08 18:25:42,2e4cf2f3-0bec-47fa-8890-86883953620a,Running,,10.64,1:03:43,5:59,10.02,769.0,182,140.0,,TomTom MySports Watch,2018-01-08-182542.gpx
+2018-01-05 17:45:58,6344fc66-c0ec-47c5-bfae-c1d2b8d599c1,Running,,11.4,1:06:31,5:50,10.28,804.0,187,150.0,,TomTom MySports Watch,2018-01-05-174558.gpx
+2018-01-02 17:41:34,04c2266b-0ff3-4789-9ebb-8e28d483a3bd,Running,,11.51,1:07:45,5:53,10.2,815.0,190,150.0,,TomTom MySports Watch,2018-01-02-174134.gpx
+2017-12-30 17:11:53,bbfe7660-45db-4b6f-982f-11ca900db7e5,Running,,11.69,1:10:16,6:01,9.98,841.0,192,150.0,,TomTom MySports Watch,2017-12-30-171153.gpx
+2017-12-27 18:50:24,c0706fc2-7192-4c28-b1f4-0553dc423ded,Running,,8.29,45:56,5:33,10.83,622.0,131,147.0,,TomTom MySports Watch,2017-12-27-185024.gpx
+2017-12-04 18:14:41,45475dee-1fba-4bc5-bdf1-1ca458d0b20a,Running,,10.0,59:43,5:58,10.05,717.0,160,142.0,,TomTom MySports Watch,2017-12-04-181441.gpx
+2017-11-25 12:17:22,402c9db6-fae4-43fe-b98b-92d474872f81,Running,,12.85,1:10:04,5:27,11.0,899.0,176,157.0,,TomTom MySports Watch,2017-11-25-121722.gpx
+2017-11-22 18:21:12,bae73a36-cb24-4137-96c2-156e569cd015,Running,,7.55,43:29,5:46,10.42,534.0,129,140.0,,TomTom MySports Watch,2017-11-22-182112.gpx
+2017-11-15 18:06:19,80ef1296-34ab-4880-af73-f7d9c8814b27,Running,,10.42,1:03:00,6:03,9.93,748.0,159,139.0,,TomTom MySports Watch,2017-11-15-180619.gpx
+2017-11-09 18:39:48,3f5756f2-b3dc-4aa7-acc3-e38d7fa8e546,Running,,9.2,53:40,5:50,10.29,656.0,155,155.0,,TomTom MySports Watch,2017-11-09-183948.gpx
+2017-11-05 15:48:01,b2fcf898-872d-48ea-8d29-29e11191c5cf,Running,,12.78,1:10:36,5:31,10.86,897.0,172,154.0,,TomTom MySports Watch,2017-11-05-154801.gpx
+2017-10-28 16:07:20,88c26827-340d-4ef9-ba4e-e3fe1fdf6da4,Running,,12.87,1:11:13,5:32,10.84,895.0,170,148.0,,TomTom MySports Watch,2017-10-28-160720.gpx
+2017-10-19 18:22:10,85c4573b-109d-404a-a3ba-a0d62bdb9ffc,Running,,8.85,50:41,5:44,10.47,624.0,144,140.0,,TomTom MySports Watch,2017-10-19-182210.gpx
+2017-10-16 17:09:27,4d1cf5d9-cfc2-4020-892c-2deee6d125ed,Running,,14.9,1:22:16,5:31,10.86,1045.0,215,146.0,,TomTom MySports Watch,2017-10-16-170927.gpx
+2017-10-10 18:24:47,a734675d-6dca-437e-8bbe-0aaec901a5f2,Running,,8.04,43:22,5:24,11.13,570.0,126,146.0,,TomTom MySports Watch,2017-10-10-182447.gpx
+2017-10-07 16:29:17,8c38fe9f-c35b-416f-b77a-038777036e03,Running,,18.67,1:48:11,5:48,10.36,1327.0,297,143.0,,TomTom MySports Watch,2017-10-07-162917.gpx
+2017-10-01 17:55:38,83409eae-5b76-47b8-89f0-db1e04801fc9,Running,,12.76,1:11:15,5:35,10.74,886.0,174,144.0,,TomTom MySports Watch,2017-10-01-175538.gpx
+2017-09-28 18:20:48,acb25a0d-251b-4d39-86cd-63d49110d02e,Running,,12.77,1:07:38,5:18,11.33,897.0,171,143.0,,TomTom MySports Watch,2017-09-28-182048.gpx
+2017-09-25 18:39:09,685a3715-03e9-42ad-af5f-4d08ae973c14,Running,,12.75,1:06:43,5:14,11.46,896.0,169,148.0,,TomTom MySports Watch,2017-09-25-183909.gpx
+2017-09-22 12:27:14,4817879b-dedb-4b3d-a2e8-92d41757ccea,Cycling,,49.18,2:42:32,3:18,18.15,852.0,367,,,,2017-09-22-122714.gpx
+2017-09-19 16:24:08,01fad411-6664-4cda-826c-044d3ad9e2bf,Running,,17.77,1:39:39,5:36,10.7,1247.0,257,139.0,,TomTom MySports Watch,2017-09-19-162408.gpx
+2017-09-16 17:26:25,2fe19360-3ce6-4740-8d45-5fe77b21df75,Running,,18.6,1:43:09,5:33,10.82,1305.0,313,138.0,,TomTom MySports Watch,2017-09-16-172625.gpx
+2017-09-10 16:57:56,fac04b5b-6fc2-45fc-bc29-63fa4eb77569,Running,,23.64,2:16:31,5:47,10.39,1682.0,394,142.0,,TomTom MySports Watch,2017-09-10-165756.gpx
+2017-09-07 18:44:44,c95ca273-6089-4f22-9292-b22e3c0a89a1,Running,,8.5,44:32,5:14,11.45,603.0,88,141.0,,TomTom MySports Watch,2017-09-07-184444.gpx
+2017-09-05 18:34:21,5fb52c7a-0331-4382-9636-b9fca42a797f,Running,,12.77,1:06:15,5:11,11.56,896.0,127,152.0,,TomTom MySports Watch,2017-09-05-183421.gpx
+2017-09-03 17:20:20,861a0fed-e63d-415e-9193-b5815e65ff9f,Running,,23.8,2:11:00,5:30,10.9,1669.0,384,151.0,,TomTom MySports Watch,2017-09-03-172020.gpx
+2017-08-31 18:44:27,51fb7201-2bba-4611-b2af-6530343ebc88,Running,,12.79,1:06:39,5:13,11.51,895.0,169,142.0,,TomTom MySports Watch,2017-08-31-184427.gpx
+2017-08-29 18:36:14,e7a75592-c649-4c99-96be-ec52d555beca,Running,,12.7,1:07:42,5:20,11.25,901.0,127,147.0,,TomTom MySports Watch,2017-08-29-183614.gpx
+2017-08-19 18:01:57,6e8cc448-7326-4986-b175-4a9341ec5d30,Running,,23.62,2:17:14,5:49,10.33,1667.0,377,138.0,,TomTom MySports Watch,2017-08-19-180157.gpx
+2017-08-17 18:36:00,3c980f93-7aa1-477c-9f90-72ca01552031,Cycling,,15.53,40:04,2:35,23.25,380.0,164,138.0,,TomTom MySports Watch,2017-08-17-183600.gpx
+2017-08-16 19:00:40,3522a99f-e715-4a5b-93fe-a681bdef0ea1,Running,,12.79,1:07:40,5:17,11.34,896.0,170,148.0,,TomTom MySports Watch,2017-08-16-190040.gpx
+2017-08-14 18:01:38,e3f350ab-c7fa-42dd-8ccc-22162be34ab7,Running,,12.77,1:08:40,5:23,11.16,893.0,171,142.0,,TomTom MySports Watch,2017-08-14-180138.gpx
+2017-08-10 18:28:54,7b11bced-0b12-4e02-9717-09587f4c715f,Running,,8.43,43:48,5:12,11.54,596.0,86,147.0,,TomTom MySports Watch,2017-08-10-182854.gpx
+2017-08-08 18:39:14,d5f7dd47-f709-437c-ac71-30e87ce7e7ab,Running,,12.56,59:37,4:45,12.64,849.0,121,158.0,,TomTom MySports Watch,2017-08-08-183914.gpx
+2017-08-05 17:14:00,57990ccc-1fc5-41b0-bc71-8b28ddf0d1b0,Running,,18.6,1:44:22,5:37,10.69,1398.0,295,142.0,,,2017-08-05-171400.gpx
+2017-08-01 18:39:42,f116bf61-c409-448a-b80d-35de131c5afd,Running,,8.4,42:50,5:06,11.77,589.0,83,156.0,,TomTom MySports Watch,2017-08-01-183942.gpx
+2017-07-30 17:24:23,13415fa0-b417-4681-969c-bcc5bc62882b,Running,,18.58,1:45:15,5:40,10.59,1312.0,307,145.0,,TomTom MySports Watch,2017-07-30-172423.gpx
+2017-07-23 17:14:16,a93a029b-ba9f-46c3-acb5-1bfbb75257b2,Running,,18.71,1:39:26,5:19,11.29,1308.0,298,144.0,,TomTom MySports Watch,2017-07-23-171416.gpx
+2017-07-19 18:50:31,ebe894be-7567-4b56-91ab-164554e20357,Running,,12.8,1:08:04,5:19,11.28,891.0,170,143.0,,TomTom MySports Watch,2017-07-19-185031.gpx
+2017-07-16 10:34:21,20b3b267-b9a2-4c64-89a3-3ba0463b582c,Running,,18.54,1:44:22,5:38,10.66,1302.0,295,142.0,,TomTom MySports Watch,2017-07-16-103421.gpx
+2017-07-10 18:26:02,8087a508-a36b-4bbd-bca3-cb458f855e9c,Running,,12.6,1:04:42,5:08,11.68,880.0,124,155.0,,TomTom MySports Watch,2017-07-10-182602.gpx
+2017-07-08 16:26:43,ad867d0a-2a2b-4358-9a7a-65c2e098e8e6,Running,,23.59,2:13:08,5:39,10.63,1668.0,390,143.0,,TomTom MySports Watch,2017-07-08-162643.gpx
+2017-07-05 18:53:40,2301d707-d4e3-461d-b6af-5ffff1b77dbf,Running,,12.78,1:07:52,5:19,11.3,875.0,173,140.0,,TomTom MySports Watch,2017-07-05-185340.gpx
+2017-07-02 18:34:53,67d8bff0-657c-46ce-aa3a-4c6533e2f883,Running,,10.25,58:05,5:40,10.59,714.0,130,138.0,,TomTom MySports Watch,2017-07-02-183453.gpx
+2017-07-02 17:15:47,f6e2f233-cf73-4d68-9938-52231df7c56a,Running,,13.39,1:16:21,5:42,10.52,938.0,248,136.0,,TomTom MySports Watch,2017-07-02-171547.gpx
+2017-06-28 18:24:55,242d25a7-3f50-4e56-a351-59cbcf3f25fc,Running,,12.7,1:07:16,5:18,11.33,883.0,171,147.0,,TomTom MySports Watch,2017-06-28-182455.gpx
+2017-06-25 15:57:36,7b4de242-4cb7-4962-8ec3-c0cbc71fb9b7,Running,,21.27,2:04:36,5:51,10.24,1518.0,340,145.0,,TomTom MySports Watch,2017-06-25-155736.gpx
+2017-06-21 18:42:29,1a03076f-c730-4d45-8415-b0fe189cc672,Running,,12.72,1:09:23,5:27,11.0,885.0,175,139.0,,TomTom MySports Watch,2017-06-21-184229.gpx
+2017-06-17 10:13:45,d4fccef8-c615-43a8-bf27-fa75bb200d13,Running,,14.87,1:25:46,5:46,10.41,1048.0,247,143.0,,TomTom MySports Watch,2017-06-17-101345.gpx
+2017-06-15 18:54:32,86cdd8ff-11c5-4c6a-b221-3e65a88cc136,Running,,12.8,1:06:15,5:10,11.59,901.0,128,147.0,,TomTom MySports Watch,2017-06-15-185432.gpx
+2017-06-12 18:25:58,ba13aabc-d722-4803-9c1f-4e5043ecced6,Running,,12.67,1:05:58,5:12,11.53,893.0,128,148.0,,TomTom MySports Watch,2017-06-12-182558.gpx
+2017-06-04 16:01:52,f607c11e-85ad-492d-9cd3-df2f41d60c24,Running,,21.02,2:02:14,5:49,10.32,1482.0,330,138.0,,TomTom MySports Watch,2017-06-04-160152.gpx
+2017-06-01 19:00:16,7937624f-091c-4ba4-933f-15c22bf65c4a,Running,,8.65,45:28,5:15,11.41,612.0,88,143.0,,TomTom MySports Watch,2017-06-01-190016.gpx
+2017-05-30 18:19:20,58569994-61c8-400c-a647-114e161ff8df,Running,,8.42,42:48,5:05,11.8,584.0,85,154.0,,TomTom MySports Watch,2017-05-30-181920.gpx
+2017-05-28 16:42:07,524ed1b8-7056-4455-be45-331b472c21f7,Running,,21.11,1:58:50,5:38,10.66,1477.0,337,143.0,,TomTom MySports Watch,2017-05-28-164207.gpx
+2017-05-24 18:23:30,719a8602-3d87-471a-b74b-0621ea4c813d,Running,,12.74,1:09:47,5:29,10.95,892.0,171,140.0,,TomTom MySports Watch,2017-05-24-182330.gpx
+2017-05-21 12:18:56,88ada870-907d-43d0-9a5a-bc92c8c6b97b,Running,,22.07,2:06:18,5:43,10.48,1569.0,354,146.0,,TomTom MySports Watch,2017-05-21-121856.gpx
+2017-05-17 18:18:34,f53266a4-2f39-4c18-ad50-6fd60ab34285,Running,,13.82,1:10:59,5:08,11.68,1000.0,128,,,,2017-05-17-181834.gpx
+2017-05-15 18:25:55,8eef0492-ce75-4cd5-8de5-f94bb69ef4dc,Running,,16.83,1:24:48,5:02,11.91,1177.0,167,151.0,,TomTom MySports Watch,2017-05-15-182555.gpx
+2017-05-10 18:40:04,0edf90b1-2417-413a-96b6-368d01df8677,Running,,8.53,43:24,5:05,11.79,595.0,87,146.0,,TomTom MySports Watch,2017-05-10-184004.gpx
+2017-05-03 18:32:01,f5dd15c6-ffbd-48e5-aa1c-3777ed5a97c7,Running,,12.43,1:04:04,5:09,11.64,865.0,125,151.0,,TomTom MySports Watch,2017-05-03-183201.gpx
+2017-05-01 17:38:35,9a5c1022-08f3-4c9b-9cc5-492d4d7178f9,Cycling,,20.19,54:29,2:42,22.24,491.0,204,135.0,,TomTom MySports Watch,2017-05-01-173835.gpx
+2017-04-29 17:05:47,0c00d186-32b8-43ff-ad2b-a982a535e270,Running,,21.18,2:02:14,5:46,10.39,1504.0,343,142.0,,TomTom MySports Watch,2017-04-29-170547.gpx
+2017-04-25 18:44:32,51eb30dc-bc11-4296-bb7d-d40c83c60319,Running,,8.68,44:46,5:09,11.63,606.0,86,150.0,,TomTom MySports Watch,2017-04-25-184432.gpx
+2017-04-23 11:50:12,943aff3a-a7a5-49c6-a179-5d5111600f01,Running,Two roads - Irpin,19.27,1:50:24,5:44,10.47,1450.0,305,149.0,,TomTom MySports Watch,2017-04-23-115012.gpx
+2017-04-17 16:20:08,51ab838c-9266-436d-b26a-eb6b0d7c0fa5,Running,,15.0,1:21:03,5:24,11.1,1060.0,222,147.0,,TomTom MySports Watch,2017-04-17-162008.gpx
+2017-04-13 19:04:16,48ea0a3a-4a02-4ee9-b310-8a367fd4e59d,Running,,8.58,46:21,5:24,11.1,603.0,88,145.0,,TomTom MySports Watch,2017-04-13-190416.gpx
+2017-04-10 18:46:56,7263bbb0-934f-47a7-9d9e-f0a0816286be,Running,,12.72,1:07:05,5:16,11.38,908.9999994764161,122,146.0,,TomTom MySports Watch,2017-04-10-184656.gpx
+2017-04-06 19:07:46,344ece5a-187e-418e-9287-0e8a11a07643,Running,,8.65,42:24,4:54,12.25,594.99999965728,87,156.0,,TomTom MySports Watch,2017-04-06-190746.gpx
+2017-04-04 18:31:11,6b2ca811-ca43-42ed-9b08-8752d2ddac50,Running,,8.61,45:58,5:20,11.24,612.999999646912,89,143.0,,TomTom MySports Watch,2017-04-04-183111.gpx
+2017-04-01 16:56:53,410ddb93-7109-4248-9ca6-86da1fdd311b,Running,,13.53,1:14:46,5:32,10.86,955.999999449344,196,146.0,,TomTom MySports Watch,2017-04-01-165653.gpx
+2017-03-30 19:06:29,9a3ec53a-d6f2-4ba7-87e1-085edf2a8064,Running,,8.48,46:53,5:32,10.85,606.999999650368,90,143.0,,TomTom MySports Watch,2017-03-30-190629.gpx
+2017-03-28 19:13:23,0b6616e6-be34-4950-b04e-11f0deeca9ab,Running,,8.65,45:24,5:15,11.43,611.999999647488,88,152.0,,TomTom MySports Watch,2017-03-28-191323.gpx
+2017-03-26 17:10:40,2c90030a-14cb-46e6-8152-646bccabe3b5,Running,,14.62,1:21:54,5:36,10.71,1062.0,149,,,,2017-03-26-171040.gpx
+2017-03-20 18:44:13,f7461f26-c148-430d-be37-c1ede0e57ec5,Running,,14.42,1:18:40,5:27,11.0,1017.9999994136301,99,148.0,,TomTom MySports Watch,2017-03-20-184413.gpx
+2017-03-16 18:21:05,02eff545-2173-439c-afc3-d446e5802528,Running,,6.8,36:18,5:20,11.24,489.9999997177599,67,151.0,,TomTom MySports Watch,2017-03-16-182105.gpx
+2017-03-13 18:34:11,a286c0fa-6876-491b-854a-c6d8813b4237,Running,,12.06,1:07:14,5:34,10.77,839.99999951616,113,143.0,,TomTom MySports Watch,2017-03-13-183411.gpx
+2017-03-09 18:20:43,cfdbf8c5-6f66-4de4-af0b-afc5f5a8ab28,Running,,9.52,54:28,5:43,10.49,675.9999996106241,81,139.0,,TomTom MySports Watch,2017-03-09-182043.gpx
+2017-02-27 18:57:30,853911d0-7a12-456d-8e72-974a8e3782e5,Running,,11.02,59:26,5:24,11.12,790.999999544384,102,150.0,,TomTom MySports Watch,2017-02-27-185730.gpx
+2017-02-23 18:39:15,97b7530f-7db6-421e-95a4-6055cf6834df,Running,,8.8,50:57,5:47,10.37,617.999999644032,85,139.0,,TomTom MySports Watch,2017-02-23-183915.gpx
+2017-02-21 19:16:07,37a6dd24-87a6-4ec7-8df2-bed0faddf4c8,Running,,7.4,41:20,5:35,10.75,521.999999699328,82,140.0,,TomTom MySports Watch,2017-02-21-191607.gpx
+2017-02-13 18:42:55,a95379e3-8981-4309-9c3e-3f4397bf432d,Running,,9.78,56:19,5:45,10.42,691.9999996014079,78,146.0,,TomTom MySports Watch,2017-02-13-184255.gpx
+2017-02-06 18:45:01,a3b20f84-2a41-4d82-ba64-ba5ae415d5a1,Running,,9.79,58:23,5:58,10.07,716.999999587008,76,145.0,,TomTom MySports Watch,2017-02-06-184501.gpx
+2017-01-30 18:43:33,dcf483e9-421e-42b6-b116-884c794f5366,Running,,6.98,37:28,5:22,11.18,496.999999713728,66,143.0,,TomTom MySports Watch,2017-01-30-184333.gpx
+2017-01-26 18:47:36,d61065b6-749d-46bf-a680-6ebb7067620c,Running,,8.3,44:30,5:22,11.19,585.999999662464,74,144.0,,TomTom MySports Watch,2017-01-26-184736.gpx
+2017-01-23 18:39:10,9a35d26a-e947-4374-b411-a28c07f0b153,Running,,11.98,1:07:49,5:40,10.6,849.9999995103999,100,150.0,,TomTom MySports Watch,2017-01-23-183910.gpx
+2017-01-19 18:10:00,e3e565d9-b63b-4a12-9704-b3daef064784,Running,,7.43,41:36,5:36,10.72,517.999999701632,51,132.0,,TomTom MySports Watch,2017-01-19-181000.gpx
+2017-01-16 18:04:30,5847c202-4eb8-4416-b641-530232791775,Running,,12.15,1:08:32,5:38,10.64,849.9999995103999,99,143.0,,TomTom MySports Watch,2017-01-16-180430.gpx
+2017-01-12 18:19:37,1a0c5ffe-b6ef-4c05-8126-acd92ab1b5a6,Running,,12.0,1:08:08,5:41,10.57,859.9999995046401,100,148.0,,TomTom MySports Watch,2017-01-12-181937.gpx
+2017-01-05 18:15:10,e6f58116-e2c7-4f3a-848e-0529635f8bb3,Running,,7.02,39:42,5:39,10.61,492.99999971603205,68,143.0,,TomTom MySports Watch,2017-01-05-181510.gpx
+2017-01-03 18:26:07,2e8b8cba-c1b5-4837-a124-9b8a30cc5ff9,Running,,10.05,57:27,5:43,10.49,699.9999995968,93,143.0,,TomTom MySports Watch,2017-01-03-182607.gpx
+2016-12-29 19:08:37,85115e40-2460-4043-ab77-9c48ad4e1aa3,Running,,7.0,39:48,5:41,10.55,490.999999717184,67,113.0,,TomTom MySports Watch,2016-12-29-190837.gpx
+2016-12-26 18:37:08,8f84f8fd-d26f-46d9-91bc-9ee4ddb6b2a8,Running,,6.92,39:42,5:44,10.45,478.99999972409597,67,139.0,,TomTom MySports Watch,2016-12-26-183708.gpx
+2016-12-22 18:43:38,94105764-bf26-4123-b199-f4efef9c9959,Running,,6.9,38:28,5:35,10.76,481.999999722368,64,137.0,,TomTom MySports Watch,2016-12-22-184338.gpx
+2016-12-15 18:53:07,ad7aec27-c204-403a-aa08-5f4068d5b239,Running,,6.85,37:44,5:30,10.9,486.999999719488,67,140.0,,TomTom MySports Watch,2016-12-15-185307.gpx
+2016-12-06 18:36:37,159d92f4-c96b-43ea-88a3-8f1060d170ed,Running,,6.99,41:34,5:57,10.08,499.999999712,49,139.0,,TomTom MySports Watch,2016-12-06-183637.gpx
+2016-12-01 18:39:39,5671ace7-1b97-4466-b4a3-e89a40588690,Running,,6.96,39:33,5:41,10.56,478.99999972409597,66,126.0,,TomTom MySports Watch,2016-12-01-183939.gpx
+2016-11-28 18:46:07,f603036c-01a9-40dd-ae80-101e70056810,Running,,10.13,57:41,5:42,10.54,708.9999995916161,95,144.0,,TomTom MySports Watch,2016-11-28-184607.gpx
+2016-11-24 18:50:24,1bfdff30-6f7f-4428-9316-3b6289bd57f3,Running,,6.73,36:01,5:21,11.21,470.999999728704,65,163.0,,TomTom MySports Watch,2016-11-24-185024.gpx
+2016-11-21 18:57:32,dcb51374-c574-4fac-8f30-d2f2080368eb,Running,,10.05,56:45,5:39,10.63,703.999999594496,96,146.0,,TomTom MySports Watch,2016-11-21-185732.gpx
+2016-11-17 18:52:21,56571fdf-a69d-4662-87f2-0a0cc0621d4e,Running,,6.95,40:32,5:50,10.28,493.999999715456,67,161.0,,TomTom MySports Watch,2016-11-17-185221.gpx
+2016-11-15 18:27:55,89e8ac21-0c79-4251-9d0b-ddef7c7cb0f3,Running,,6.94,40:44,5:52,10.22,491.999999716608,66,134.0,,TomTom MySports Watch,2016-11-15-182755.gpx
+2016-11-10 18:31:03,e29a6eb4-cd05-4dab-ac97-39ee3ecf0555,Running,,6.83,40:03,5:52,10.22,483.999999721216,67,134.0,,TomTom MySports Watch,2016-11-10-183103.gpx
+2016-11-03 18:17:35,912e75d6-3060-44c9-b67a-b2510b1fad32,Running,,6.77,37:58,5:37,10.7,482.999999721792,64,142.0,,TomTom MySports Watch,2016-11-03-181735.gpx
+2016-10-31 19:34:32,86ea4944-dc1b-4cc9-8e20-eb3a0267e3ff,Running,,10.15,56:36,5:35,10.76,700.999999596224,109,132.0,,TomTom MySports Watch,2016-10-31-193432.gpx
+2016-10-27 19:17:55,f7222eab-f4cf-4f15-ab3e-fb6a723b1294,Running,,6.84,34:57,5:06,11.75,477.999999724672,75,146.0,,TomTom MySports Watch,2016-10-27-191755.gpx
+2016-10-24 18:38:31,7d043cea-6c58-4e18-8d31-6ac3f1deb2a8,Running,,10.15,58:25,5:45,10.42,703.999999594496,92,139.0,,TomTom MySports Watch,2016-10-24-183831.gpx
+2016-10-20 18:57:48,772cc020-ef53-429b-9ae7-43e8cc474d86,Running,,10.19,56:25,5:32,10.83,715.999999587584,89,148.0,,TomTom MySports Watch,2016-10-20-185748.gpx
+2016-10-17 18:39:56,605ee6a1-3eba-4b09-8528-66f0a34368d5,Running,,10.02,55:56,5:35,10.75,712.999999589312,92,136.0,,TomTom MySports Watch,2016-10-17-183956.gpx
+2016-10-15 17:14:34,467ce511-b823-4bf8-a6b8-3de45da696b9,Running,,13.49,1:14:11,5:30,10.91,942.9999994568319,182,143.0,,TomTom MySports Watch,2016-10-15-171434.gpx
+2016-10-12 17:50:31,0762a080-72c1-4316-9af3-520de54776dd,Running,,9.77,52:12,5:21,11.22,700.0,57,,,,2016-10-12-175031.gpx
+2016-10-03 11:47:16,d7f518d9-57aa-4c3f-b5d1-a3f758155737,Cycling,,23.62,1:12:42,3:05,19.5,606.0,301,126.0,,TomTom MySports Watch,2016-10-03-114716.gpx
+2016-10-02 16:36:05,f9df1129-32bf-4ff8-93fa-8e2617617c37,Running,,13.43,1:12:20,5:23,11.14,945.9999994551041,184,141.0,,TomTom MySports Watch,2016-10-02-163605.gpx
+2016-09-27 18:11:27,e47410e1-1a6c-428f-a03d-7f5c86a1e6e7,Running,,6.88,32:47,4:46,12.59,496.0,49,,,,2016-09-27-181127.gpx
+2016-09-24 16:39:37,90d59463-bd07-4aa2-bf45-51c5cc985a1f,Running,,19.41,1:46:24,5:29,10.95,1344.9999992252801,320,140.0,,TomTom MySports Watch,2016-09-24-163937.gpx
+2016-09-22 18:52:22,1454fad1-9940-448a-82fb-8ae9f3bf05e5,Running,,6.96,37:14,5:21,11.22,493.999999715456,63,135.0,,TomTom MySports Watch,2016-09-22-185222.gpx
+2016-09-20 18:42:40,b6743512-8841-4278-9f54-8028ca96eae6,Running,,10.03,53:21,5:19,11.28,712.999999589312,89,139.0,,TomTom MySports Watch,2016-09-20-184240.gpx
+2016-09-15 18:38:53,27a3ae81-deee-44f8-97ce-da1ed9d11ab6,Running,,10.0,47:55,4:47,12.52,673.9999996117759,91,150.0,,TomTom MySports Watch,2016-09-15-183853.gpx
+2016-09-12 18:23:04,76fd4f11-78e0-4c31-a25f-4933c9163901,Running,,13.08,1:10:33,5:24,11.12,916.999999471808,116,141.0,,TomTom MySports Watch,2016-09-12-182304.gpx
+2016-09-10 17:13:51,be77ab12-11b2-4604-828d-6ac7ee0b6c50,Cycling,,13.11,32:47,2:30,23.99,328.0,163,136.0,,TomTom MySports Watch,2016-09-10-171351.gpx
+2016-09-08 18:19:56,8a409a09-8312-448c-b513-ea63a18cbca1,Running,,13.12,1:04:40,4:56,12.17,905.9999994781441,110,154.0,,TomTom MySports Watch,2016-09-08-181956.gpx
+2016-09-04 17:08:28,cc9e2d95-fc5d-4633-9801-a6a92df8eebe,Cycling,,13.64,40:43,2:59,20.09,342.0,184,134.0,,TomTom MySports Watch,2016-09-04-170828.gpx
+2016-09-03 17:28:11,9697ecb1-cfe8-454c-86c6-1def815159b2,Running,,13.5,1:17:44,5:46,10.42,948.9999994533761,184,138.0,,TomTom MySports Watch,2016-09-03-172811.gpx
+2016-08-31 18:33:50,d2e170c7-70e0-430c-ad0a-185fc87a4dae,Running,,13.06,1:04:09,4:55,12.22,898.999999482176,107,152.0,,TomTom MySports Watch,2016-08-31-183350.gpx
+2016-08-28 18:00:16,3054ec70-ce18-45a0-aaa3-badaa9d89711,Running,,12.05,1:07:14,5:35,10.76,834.9999995190401,172,143.0,,TomTom MySports Watch,2016-08-28-180016.gpx
+2016-08-27 17:08:38,e3fc3373-0a83-4dba-8042-7eca0b3cf676,Cycling,,24.67,1:08:53,2:48,21.49,623.0,307,134.0,,TomTom MySports Watch,2016-08-27-170838.gpx
+2016-08-24 17:26:00,81b6ef5d-8f11-4c2f-966d-d253706aa921,Running,,13.25,1:11:57,5:26,11.05,897.9999994827521,181,141.0,,TomTom MySports Watch,2016-08-24-172600.gpx
+2016-08-22 18:14:48,ee6099f2-aa7e-4607-9934-829a12fd5224,Running,,10.06,51:20,5:06,11.76,716.0,58,,,,2016-08-22-181448.gpx
+2016-08-20 15:43:49,d71c6646-114b-4fb9-a011-d195c560591b,Cycling,,31.48,1:29:50,2:51,21.03,740.0,553,138.0,,TomTom MySports Watch,2016-08-20-154349.gpx
+2016-08-19 16:57:41,454b61da-b805-4a17-9a46-940c3607e8ee,Running,,13.43,1:14:24,5:32,10.83,904.9999994787199,185,150.0,,TomTom MySports Watch,2016-08-19-165741.gpx
+2016-08-17 17:17:09,af644132-ea3f-4770-a8d3-703c17d6c858,Cycling,,21.82,1:22:34,3:47,15.86,533.0,316,122.0,,TomTom MySports Watch,2016-08-17-171709.gpx
+2016-08-14 12:32:07,a871f64a-5aba-44ce-b166-f3b57c4406a6,Running,,21.16,2:04:44,5:54,10.18,1469.99999915328,351,149.0,,TomTom MySports Watch,2016-08-14-123207.gpx
+2016-08-01 17:40:40,513f1448-16e1-4cc3-8f1f-71c02809d8c2,Running,,14.32,1:20:36,5:38,10.66,997.9999994251519,213,151.0,,TomTom MySports Watch,2016-08-01-174040.gpx
+2016-07-24 17:13:38,34994797-0b06-484c-8a7a-ebd8760084a9,Running,,20.81,2:03:35,5:56,10.11,1435.99999917286,367,144.0,,TomTom MySports Watch,2016-07-24-171338.gpx
+2016-07-18 18:28:47,d937fa3d-ace4-4083-abaa-ce450bf92ea9,Running,,10.01,50:49,5:05,11.82,682.999999606592,92,151.0,,TomTom MySports Watch,2016-07-18-182847.gpx
+2016-07-16 17:27:12,5085cce1-bebb-48cb-8429-33ff77b6fdf3,Running,,22.06,2:08:28,5:49,10.3,1524.9999991216,354,143.0,,TomTom MySports Watch,2016-07-16-172712.gpx
+2016-07-12 18:20:28,d1b3933f-2b58-4158-b2e7-b2385682da43,Running,,9.71,50:45,5:14,11.47,696.0,52,,,,2016-07-12-182028.gpx
+2016-07-10 16:35:25,85014381-7900-49e2-88f0-42c75e0954c1,Running,,19.27,1:49:48,5:42,10.53,1323.99999923738,319,142.0,,TomTom MySports Watch,2016-07-10-163525.gpx
+2016-07-04 18:24:46,b8d35359-b7ee-4270-bd59-0b108b86bafd,Running,,16.46,1:28:53,5:24,11.11,1139.99999934336,146,144.0,,TomTom MySports Watch,2016-07-04-182446.gpx
+2016-07-02 17:52:07,04a5969e-40b1-45de-a8f4-79745c556c59,Running,,13.55,1:18:01,5:46,10.42,933.999999462016,189,137.0,,TomTom MySports Watch,2016-07-02-175207.gpx
+2016-06-27 18:40:44,20ef6c75-37e5-48cc-ab4a-934347347632,Running,,13.28,1:12:43,5:29,10.96,903.999999479296,180,146.0,,TomTom MySports Watch,2016-06-27-184044.gpx
+2016-06-25 17:02:44,c306e91d-482d-40b4-96f6-c6f2117eaab4,Running,,13.22,1:16:38,5:48,10.35,917.999999471232,226,142.0,,TomTom MySports Watch,2016-06-25-170244.gpx
+2016-06-23 18:29:58,7bedfff1-e270-4ca6-9b34-61f3fc016487,Running,,10.02,51:35,5:09,11.65,719.0,60,,,"Very hot, up to 30+
+",2016-06-23-182958.gpx
+2016-06-19 17:04:31,2c7b0c7c-1a8e-46d8-88f0-f52cde314a5d,Running,,18.59,1:49:24,5:53,10.19,1283.9999992604198,295,142.0,,TomTom MySports Watch,2016-06-19-170431.gpx
+2016-06-14 18:40:51,86208dc5-01ec-4383-8284-89afd17ff565,Running,,13.21,1:08:35,5:11,11.56,904.9999994787199,119,155.0,,TomTom MySports Watch,2016-06-14-184051.gpx
+2016-06-12 12:07:08,5ba46d07-d64a-4204-b64c-11b5fb095be6,Running,,19.89,1:45:59,5:20,11.26,1452.0,202,,,,2016-06-12-120708.gpx
+2016-06-05 17:10:29,adb38948-116e-4000-9598-877aa7b718da,Running,,15.52,1:30:35,5:50,10.28,1073.99999938138,254,142.0,,TomTom MySports Watch,2016-06-05-171029.gpx
+2016-06-02 18:43:44,7d056151-5c10-44c8-ad58-43bf7e1e6fe6,Running,,13.24,1:09:06,5:13,11.5,910.999999475264,117,145.0,,TomTom MySports Watch,2016-06-02-184344.gpx
+2016-05-30 18:51:32,69ba4306-6177-4ef1-b3c6-cea3e9700116,Running,,10.03,52:02,5:11,11.57,685.999999604864,91,145.0,,TomTom MySports Watch,2016-05-30-185132.gpx
+2016-05-26 18:30:37,e6a0852a-16ff-446c-b522-21c9489c07b7,Running,,12.88,1:08:16,5:18,11.32,892.999999485632,145,149.0,,TomTom MySports Watch,2016-05-26-183037.gpx
+2016-05-23 18:26:58,bf304a1c-a01b-4ceb-9559-bde274612398,Running,,10.02,51:25,5:08,11.68,720.0,64,,,,2016-05-23-182658.gpx
+2016-05-22 15:31:06,d6e2c045-ce5b-49be-846e-a59df6bd4f0d,Cycling,,39.62,1:48:59,2:45,21.81,877.0,319,,,,2016-05-22-153106.gpx
+2016-05-19 18:54:07,fc27d591-a215-4c9b-b30d-22dc38a9293e,Running,,10.14,55:00,5:26,11.06,701.9999995956481,99,145.0,,TomTom MySports Watch,2016-05-19-185407.gpx
+2016-05-09 16:46:49,6efb6c1b-fcb4-449c-be9b-6c70d6d0acab,Running,,13.5,1:15:04,5:34,10.79,920.9999994695039,181,147.0,,TomTom MySports Watch,2016-05-09-164649.gpx
+2016-04-24 17:16:01,3c60a550-2443-4986-b9b8-666f29b2b367,Running,,12.85,1:16:56,5:59,10.02,902.999999479872,185,142.0,,TomTom MySports Watch,2016-04-24-171601.gpx
+2016-04-17 16:20:56,5fe9a80e-d885-426e-816e-2d7aca6844ec,Running,,13.47,1:17:55,5:47,10.37,927.999999465472,180,140.0,,TomTom MySports Watch,2016-04-17-162056.gpx
+2016-04-11 18:25:11,f5856eb2-a21b-4796-9b0c-b581bfb0f777,Running,,8.88,47:00,5:17,11.34,615.9999996451841,83,143.0,,TomTom MySports Watch,2016-04-11-182511.gpx
+2016-04-07 18:50:38,f260fb9a-5017-40e2-af1d-77ff22e3c5f6,Running,,10.0,51:15,5:07,11.71,684.99999960544,92,145.0,,TomTom MySports Watch,2016-04-07-185038.gpx
+2016-04-03 16:27:37,30839785-9325-435a-97d2-219039984113,Running,,10.3,58:59,5:44,10.47,705.999999593344,143,142.0,,TomTom MySports Watch,2016-04-03-162737.gpx
+2016-03-28 16:12:15,bd2a55a4-2553-4007-808d-5456de4482c6,Running,,15.62,1:32:26,5:55,10.14,1084.99999937504,258,145.0,,TomTom MySports Watch,2016-03-28-161215.gpx
+2016-03-21 18:51:35,63c7f05c-2285-489e-ae1e-af1b7ecce87c,Running,,9.1,51:25,5:39,10.62,625.999999639424,85,142.0,,TomTom MySports Watch,2016-03-21-185135.gpx
+2016-03-13 13:02:38,c1105c54-b975-486c-8606-9e0a80994541,Running,,7.19,43:26,6:03,9.93,511.999999705088,111,136.0,,TomTom MySports Watch,2016-03-13-130238.gpx
+2016-03-07 13:23:44,375d191f-2d90-4781-8223-a2a8146ca445,Running,,13.69,1:20:39,5:54,10.18,995.0,141,,,,2016-03-07-132344.gpx
+2016-02-28 13:37:46,37908beb-2ae0-41e7-9554-3662d9beefec,Running,,13.48,1:19:28,5:54,10.18,941.999999457408,183,144.0,,TomTom MySports Watch,2016-02-28-133746.gpx
+2016-02-21 15:48:48,ba4a23cd-25ef-43ea-84c4-fb8c3c1e3b57,Running,,13.69,1:22:23,6:01,9.97,997.0,144,,,"Cold and windy
+",2016-02-21-154848.gpx
+2016-02-18 19:09:01,6b831381-6425-4a66-ada4-7f57f013ea05,Running,,6.92,39:25,5:42,10.54,472.99999972755205,67,141.0,,TomTom MySports Watch,2016-02-18-190901.gpx
+2016-02-15 18:37:04,c41ee42c-c911-4367-8594-e86cffbec427,Running,,10.02,1:00:08,6:00,10.0,702.999999595072,92,157.0,,TomTom MySports Watch,2016-02-15-183704.gpx
+2016-02-08 18:09:03,09404e9f-88f0-4a78-8df8-e7a51c5246e0,Running,,6.81,41:18,6:04,9.89,488.999999718336,68,165.0,,TomTom MySports Watch,2016-02-08-180903.gpx
+2016-02-04 18:46:32,baed89f0-0754-4b54-a5cb-7959c39459c0,Running,,6.84,40:02,5:51,10.25,477.999999724672,67,139.0,,TomTom MySports Watch,2016-02-04-184632.gpx
+2016-01-14 18:40:08,f5f333b1-3453-4555-aafd-d19923d0b84e,Running,,7.0,41:18,5:54,10.17,486.999999719488,70,142.0,,TomTom MySports Watch,2016-01-14-184008.gpx
+2015-12-27 13:14:59,7c59b1da-9dfd-43df-8170-a0bc4ad2bdb6,Running,,13.53,1:16:48,5:41,10.57,921.9999994689281,185,152.0,,TomTom MySports Watch,2015-12-27-131459.gpx
+2015-12-23 18:45:03,49f7059a-97c3-499e-9f42-85df1506aa90,Running,,10.12,59:02,5:50,10.29,692.9999996008321,95,158.0,,TomTom MySports Watch,2015-12-23-184503.gpx
+2015-12-21 18:31:25,620a65d8-185f-40f4-8967-9ce068f02269,Running,,10.02,57:55,5:47,10.38,686.9999996042881,91,138.0,,TomTom MySports Watch,2015-12-21-183125.gpx
+2015-12-06 12:54:29,2c3636ee-cb13-4f41-80c7-2373427859ac,Running,,13.77,1:18:04,5:40,10.58,1009.0,146,,,,2015-12-06-125429.gpx
+2015-11-17 18:37:11,5996f583-c53c-4ef3-867e-76db1c54dd6d,Running,,10.0,58:32,5:51,10.25,687.999999603712,93,140.0,,TomTom MySports Watch,2015-11-17-183711.gpx
+2015-11-09 18:21:31,df774be0-d4a3-4104-a9e4-ee120e17bcba,Running,,9.72,55:01,5:40,10.6,658.999999620416,89,131.0,,TomTom MySports Watch,2015-11-09-182131.gpx
+2015-11-02 18:23:37,bd307ec2-4c6d-4be5-b7d1-d15fb11a7b9d,Running,,6.6,36:15,5:30,10.92,455.99999973734396,62,145.0,,TomTom MySports Watch,2015-11-02-182337.gpx
+2015-10-18 15:43:06,340e4fd4-6817-4a03-b1af-7c2deb9768ae,Cycling,,16.67,1:08:46,4:08,14.54,427.0,166,,,,2015-10-18-154306.gpx
+2015-10-04 10:15:57,2ff0ef92-5476-4321-8f24-49a6b92433f3,Running,,38.32,4:28:19,7:00,8.57,2587.9999985093104,646,140.0,,TomTom MySports Watch,2015-10-04-101557.gpx
+2015-09-28 18:21:21,16bb07d0-9417-428e-b30e-2e7286b2d25a,Running,,18.94,1:44:26,5:31,10.88,1300.99999925062,158,138.0,,TomTom MySports Watch,2015-09-28-182121.gpx
+2015-09-24 18:28:14,949d5983-73ea-4a25-a589-87e0280b1b8e,Running,,9.43,51:16,5:26,11.03,649.9999996256,77,138.0,,TomTom MySports Watch,2015-09-24-182814.gpx
+2015-09-21 18:13:10,fc1187a9-6b18-4f14-b37a-ecb82a51e9d6,Running,,9.65,46:56,4:52,12.33,635.9999996336641,83,153.0,,TomTom MySports Watch,2015-09-21-181310.gpx
+2015-09-18 18:39:38,3273b8d6-96a2-4162-99d4-5277ebe826ce,Running,,22.15,2:01:30,5:29,10.94,1543.9999991106602,182,149.0,,TomTom MySports Watch,2015-09-18-183938.gpx
+2015-09-16 17:59:37,249d96bc-0b86-4602-b6cf-6fa40cf5006f,Running,,19.35,1:46:38,5:31,10.89,1299.9999992512,303,146.0,,TomTom MySports Watch,2015-09-16-175937.gpx
+2015-09-13 17:14:13,d9544e4a-4567-4a19-8bfe-4ef938d95a1c,Running,,23.95,2:23:03,5:58,10.04,1657.9999990449899,390,140.0,,TomTom MySports Watch,2015-09-13-171413.gpx
+2015-09-09 18:37:26,1977cad0-afd4-450c-9355-8aeabae10a7c,Running,,15.88,1:24:05,5:18,11.33,1101.99999936525,132,145.0,,TomTom MySports Watch,2015-09-09-183726.gpx
+2015-09-07 18:47:06,e005376b-b79c-45de-8b2e-a924dd9aa3d8,Running,,15.62,1:22:49,5:18,11.32,1069.99999938368,241,144.0,,TomTom MySports Watch,2015-09-07-184706.gpx
+2015-09-03 18:49:40,773f4ab1-e42e-4acb-b9ee-3e21f2283849,Running,,9.55,53:21,5:35,10.74,653.9999996232959,85,127.0,,TomTom MySports Watch,2015-09-03-184940.gpx
+2015-08-24 17:21:48,0730fe8a-08a3-4d22-9728-60bdccc06124,Running,,28.14,2:46:07,5:54,10.17,1971.99999886413,518,138.0,,TomTom MySports Watch,2015-08-24-172148.gpx
+2015-08-23 17:35:47,9f29e8bd-7885-43f5-bc86-fae947a5ab8a,Cycling,,35.09,1:32:58,2:39,22.64,786.0,336,,,,2015-08-23-173547.gpx
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+2014-08-28 18:10:00,9642232e-00a5-4e7d-8a92-a8766a7b5b01,Running,,12.82,1:05:45,5:08,11.7,920.0,70,,,,2014-08-28-181000.gpx
+2014-08-26 18:16:03,1cdfd12b-09c4-4b95-a0da-45b53b8129ac,Running,,16.06,1:21:39,5:05,11.8,1151.0,89,,,,2014-08-26-181603.gpx
+2014-08-21 18:49:00,8f3de547-78e3-4d6c-a467-1705d536129b,Running,,9.55,53:54,5:39,10.63,684.0,51,,,,2014-08-21-184900.gpx
+2014-08-18 18:37:00,5284fd7d-9fde-46b7-9f63-c0645dfa07ef,Running,,12.82,1:05:44,5:08,11.7,920.0,70,,,,2014-08-18-183700.gpx
+2014-08-12 18:50:00,b4f32f79-e995-4610-b584-932134dac4c4,Running,,9.55,53:54,5:39,10.63,684.0,51,,,HOT!,2014-08-12-185000.gpx
+2014-08-08 08:00:16,f9fe4f45-6f49-4b73-8336-c98413b25ac5,Running,,9.82,50:20,5:08,11.7,703.0,62,,,,2014-08-08-080016.gpx
+2014-07-25 18:35:00,779e140d-38cb-49e5-b340-ee0db1b9a3eb,Running,,13.17,1:05:08,4:57,12.13,925.0,83,,,,2014-07-25-183500.gpx
+2014-07-22 18:08:00,3faa84dd-552a-46b5-a2c8-b0a87123be2a,Running,,12.75,59:41,4:41,12.81,889.0,71,,,,2014-07-22-180800.gpx
+2014-07-17 18:30:00,e89d7bfb-2e0d-4274-987f-eaa56f06e1e2,Running,,9.55,53:54,5:39,10.63,684.0,51,,,"It was not easy, as it was +29 Celcius",2014-07-17-183000.gpx
+2014-07-10 18:45:00,47833e3a-1049-4fc9-97f1-e3a41cf1a722,Running,,12.75,1:00:41,4:46,12.6,884.0,71,,,"It was raining heavily, which was absolute fun to run that distance",2014-07-10-184500.gpx
+2014-07-02 18:31:00,0fcef878-d446-45cf-aec2-61bd86740501,Running,,12.75,1:02:41,4:55,12.2,888.0,71,,,,2014-07-02-183100.gpx
+2014-06-24 18:12:00,93c308f7-14b5-4f27-b030-7d44e223cd54,Running,,16.21,1:21:46,5:03,11.89,1145.0,85,,,,2014-06-24-181200.gpx
+2014-06-18 18:29:00,1da0fc26-5121-4528-a25d-be8e6897b047,Running,,16.21,1:21:46,5:03,11.89,1145.0,85,,,,2014-06-18-182900.gpx
+2014-06-11 18:08:00,8a7f85e2-f89f-4fed-93c3-a5c50779d32f,Running,,12.75,1:02:41,4:55,12.2,895.0,71,,,,2014-06-11-180800.gpx
+2014-06-04 18:07:39,0f94765c-b3ea-4400-9dbf-3f6aa534b94b,Running,,16.17,1:20:57,5:00,11.98,1146.0,93,,,,2014-06-04-180739.gpx
+2014-05-26 18:30:00,094db0ab-834e-41e2-89c5-162f88297e35,Running,,9.55,53:54,5:39,10.63,684.0,51,,,,2014-05-26-183000.gpx
+2014-05-21 18:45:00,3ed219e7-77f6-4981-920b-b19d3246e26b,Running,,12.75,1:01:41,4:50,12.4,893.0,71,,,,2014-05-21-184500.gpx
+2014-05-19 18:15:00,0a3dbdc0-61ef-48f2-9493-f1bd09a77ca4,Running,,12.75,1:03:01,4:57,12.14,895.0,71,,,,2014-05-19-181500.gpx
+2014-05-17 07:50:00,faf85ff4-89f1-4f2d-bd73-0194b745deba,Running,,6.56,35:23,5:24,11.12,470.0,36,,,,2014-05-17-075000.gpx
+2014-05-14 18:09:11,a9560e7b-7e4a-453c-a2ae-04752f26076b,Running,,13.17,1:05:08,4:57,12.13,930.0,84,,,,2014-05-14-180911.gpx
+2014-05-12 18:06:22,07034064-71cd-449e-9e4e-04320cc46555,Running,,12.63,1:01:22,4:51,12.35,906.0,69,,,,2014-05-12-180622.gpx
+2014-05-06 18:32:00,cbe17784-1222-446e-9101-86212817b116,Running,,12.85,1:05:41,5:07,11.74,903.0,71,,,,2014-05-06-183200.gpx
+2014-05-02 18:47:02,18a3a5a6-69e6-435f-8d5b-1e80640fa7b3,Running,,7.08,37:56,5:21,11.2,537.0,127,,,,2014-05-02-184702.gpx
+2014-04-27 10:00:00,a0c970d4-cfa5-4db9-8532-8e6d5b43d06f,Running,,21.73,1:46:38,4:54,12.23,1543.0,22,,,,2014-04-27-100000.gpx
+2014-04-21 19:08:29,03b72419-2aeb-40ce-afb5-70f5ca298984,Running,,12.75,59:41,4:41,12.81,908.0,71,,,,2014-04-21-190829.gpx
+2014-04-16 18:22:23,ae983e43-fdbe-499c-bc11-67fa63fdc1fe,Running,,19.08,1:35:03,4:59,12.04,1361.0,111,,,,2014-04-16-182223.gpx
+2014-04-14 18:19:40,f32f311f-1a02-44c3-8bc8-efbcd2f33aef,Running,,12.83,1:04:32,5:02,11.92,891.0,71,,,,2014-04-14-181940.gpx
+2014-04-09 18:29:04,dd153376-3f4d-4b58-83d8-5e2a50773288,Running,,16.21,1:21:45,5:03,11.9,1145.0,85,,,,2014-04-09-182904.gpx
+2014-04-07 18:28:42,9e71740b-bdeb-44d0-987f-12bf6d5a43b9,Running,,12.57,1:04:09,5:06,11.75,901.0,73,,,,2014-04-07-182842.gpx
+2014-04-02 18:30:00,a189ac9e-f156-4e0a-9227-6c3c5907ae87,Running,,12.47,1:04:33,5:11,11.59,895.0,69,,,,2014-04-02-183000.gpx
+2014-04-02 18:18:43,2a58f421-94a3-4e02-a4af-cf5e1fc25c9c,Running,,6.22,32:00,5:09,11.65,446.0,33,,,,2014-04-02-181843.gpx
+2014-03-24 18:56:12,1f2a8c46-872a-4edd-9055-95fcc0457be5,Running,,6.32,36:30,5:47,10.39,455.0,36,,,,2014-03-24-185612.gpx
+2014-03-20 18:49:11,54b15891-50ae-4885-a415-3924557d5e21,Running,,9.55,53:54,5:39,10.63,684.0,51,,,,2014-03-20-184911.gpx
+2014-03-18 18:49:39,920faab9-c2bf-46c0-8180-8a7039d43e9d,Running,,9.71,57:20,5:55,10.15,672.0,53,,,,2014-03-18-184939.gpx
+2014-03-13 18:41:49,f9aa0165-eb95-414e-bde5-9b26aa006347,Running,,8.62,51:36,5:59,10.02,613.0,48,,,,2014-03-13-184149.gpx
+2014-03-11 19:14:15,05a608e9-0ca9-4250-9a3c-2c8b3d8965d6,Running,,6.35,36:54,5:49,10.33,455.0,34,,,,2014-03-11-191415.gpx
+2014-03-04 18:45:09,7e0966e7-e843-4a6f-a0a7-4b323a69b526,Running,,9.45,53:34,5:40,10.58,677.0,52,,,,2014-03-04-184509.gpx
+2014-02-28 18:34:12,031d04da-435b-4b17-a27f-233242290f13,Running,,6.34,36:02,5:41,10.56,454.0,32,,,,2014-02-28-183412.gpx
+2014-02-25 18:23:39,271e28ad-4f6f-4710-9973-07fb70ac4d42,Running,,6.33,36:09,5:43,10.51,454.0,35,,,,2014-02-25-182339.gpx
+2014-02-19 18:40:00,16aafe53-0259-43f3-a5b6-7c7ffea90e52,Running,,6.38,36:10,5:40,10.58,458.0,34,,,,2014-02-19-184000.gpx
+2014-02-15 08:55:15,9d8d9396-b49a-439d-a31d-c300bcd7b621,Running,,6.29,34:34,5:30,10.91,449.0,35,,,,2014-02-15-085515.gpx
+2014-02-13 18:08:01,e8c1276e-5ecd-4b6f-bc06-72bab7efcc29,Running,,6.38,38:00,5:57,10.08,458.0,34,,,,2014-02-13-180801.gpx
+2014-02-06 06:32:00,d2db35ca-1373-45d5-a1ef-a88d5aba63ad,Running,,6.56,42:23,6:28,9.29,474.0,36,,,,2014-02-06-063200.gpx
+2014-01-07 18:13:04,0bb7d5c0-9f1e-4c7c-9842-29ae605e3204,Running,,10.81,1:01:31,5:42,10.54,774.0,56,,,,2014-01-07-181304.gpx
+2014-01-02 09:45:05,e721a5b1-59ab-4e99-851c-cbc9eee2009f,Running,,3.38,18:10,5:23,11.16,241.0,16,,,,2014-01-02-094505.gpx
+2013-12-31 08:32:35,e42c97c6-742c-4065-aad3-1c82477af7d0,Running,,6.57,36:02,5:29,10.93,471.0,36,,,,2013-12-31-083235.gpx
+2013-12-23 18:59:00,806866ca-023b-4c68-98ff-17af1d765e2f,Running,,9.5,54:08,5:42,10.53,676.0,52,,,,2013-12-23-185900.gpx
+2013-12-21 07:39:48,40fa8105-04cd-491a-a4f6-e32ed2849436,Running,,6.34,36:02,5:41,10.56,454.0,33,,,,2013-12-21-073948.gpx
+2013-12-19 18:58:42,9627ce3e-458a-4cb8-b3f0-64d6b71dee2e,Running,,9.5,53:06,5:35,10.73,679.0,52,,,,2013-12-19-185842.gpx
+2013-12-07 09:15:00,cb9e2c89-18ae-49b3-a028-d17748326a8c,Running,,6.3,34:59,5:33,10.8,452.0,35,,,,2013-12-07-091500.gpx
+2013-12-04 18:36:22,0a1d041d-9ea5-438a-a2df-dcac74a33116,Running,,12.48,1:10:42,5:40,10.59,896.0,69,,,,2013-12-04-183622.gpx
+2013-11-30 09:06:01,07f223e5-b7d2-49b6-b6b1-94b816cef1ac,Running,,6.33,34:24,5:26,11.03,451.0,35,,,,2013-11-30-090601.gpx
+2013-11-20 18:18:12,40dd6d44-18d2-4f89-bc34-e1360590b89e,Running,,11.73,1:06:41,5:41,10.55,840.0,63,,,,2013-11-20-181812.gpx
+2013-11-16 09:00:24,14e9f43f-6868-41dc-9c87-48b4206330fd,Running,,6.15,32:46,5:20,11.26,424.0,21,,,,
+2013-11-07 19:00:59,0bf65ae6-1658-4bc2-9971-5dc20ac44eb0,Running,,9.47,50:07,5:18,11.33,679.0,53,,,,2013-11-07-190059.gpx
+2013-11-02 09:15:18,36627858-ea69-40e5-9ed5-689e38e980cd,Running,,3.14,18:23,5:51,10.25,226.0,18,,,,2013-11-02-091518.gpx
+2013-10-30 18:42:16,4d8f1e78-73bb-4cac-9ce7-6167fbca9da9,Running,,5.81,33:12,5:43,10.49,413.0,32,,,,2013-10-30-184216.gpx
+2013-10-23 18:51:35,929e7c6a-ef2f-44d1-b4a9-d0159e5aa618,Running,,6.3,35:41,5:40,10.59,453.0,35,,,,2013-10-23-185135.gpx
+2013-10-18 18:07:56,eb690f23-7d74-4d07-853f-58a8a3872f19,Running,,12.53,1:09:46,5:34,10.77,897.0,74,,,,2013-10-18-180756.gpx
+2013-10-15 18:28:14,9aef5085-2cab-4872-8797-85794667ba23,Running,,8.57,49:35,5:47,10.37,616.0,45,,,,2013-10-15-182814.gpx
+2013-10-12 09:36:00,2c71d785-7846-47f1-81f0-55ea80e6e22c,Running,,6.36,34:21,5:24,11.11,455.0,35,,,,2013-10-12-093600.gpx
+2013-10-10 18:08:08,178f39db-9acd-4833-b57c-851fb430e5d4,Running,,9.34,50:21,5:24,11.12,669.0,52,,,,2013-10-10-180808.gpx
+2013-10-05 07:36:47,981fe91d-aa68-4bb8-8009-71e62dbe5d34,Running,,6.36,34:21,5:24,11.1,455.0,35,,,,2013-10-05-073647.gpx
+2013-09-30 18:50:38,f50f24f1-b79e-4bf3-8dfc-6b43d46f385f,Running,,6.45,32:41,5:04,11.84,462.0,34,,,,2013-09-30-185038.gpx
+2013-09-10 18:13:42,f696092e-4359-44fb-9544-da83ddbfc1c4,Running,,12.72,1:01:54,4:52,12.33,1018.0,398,,,,2013-09-10-181342.gpx
+2013-09-02 19:10:44,42adb02c-8d84-43fc-9612-0a7e0b4256a5,Running,,6.41,32:23,5:03,11.88,458.0,34,,,,2013-09-02-191044.gpx
+2013-08-17 08:09:12,7f7de28d-ae25-47e7-b018-bb2e903d98b2,Running,,6.45,31:22,4:52,12.32,449.0,30,,,,2013-08-17-080912.gpx
+2013-08-15 18:49:50,666cbe78-b3d5-4bbb-8a22-2717507d32c2,Walking,,2.48,2:23:46,57:56,1.04,306.0,67,,,,2013-08-15-184950.gpx
+2013-08-14 18:17:00,fcd7295f-2a3d-4d55-bcf3-6916c9ecf699,Running,,15.61,1:25:55,5:30,10.9,1121.0,88,,,,2013-08-14-181700.gpx
+2013-08-08 18:30:36,00f40213-297d-44f0-8311-a6254ac6fa4f,Running,,12.47,1:06:33,5:20,11.24,891.0,61,,,,2013-08-08-183036.gpx
+2013-08-08 07:56:08,9ea3ec6e-48fa-417f-8bdf-2b197e19f5d4,Walking,,1.51,15:24,10:11,5.89,85.0,6,,,,2013-08-08-075608.gpx
+2013-08-04 08:27:54,7ed0a8cb-0253-4ee4-aa39-3e2c444121ce,Running,,12.49,1:06:24,5:19,11.29,896.0,68,,,,2013-08-04-082754.gpx
+2013-07-30 18:49:22,e62b5ac5-e900-4c30-9f93-5f6a11da04f8,Running,,12.47,1:05:43,5:16,11.39,893.0,67,,,,2013-07-30-184922.gpx
+2013-07-21 07:57:30,924ee968-8f2c-47d9-a005-dbc732966fab,Running,,11.87,1:00:37,5:07,11.74,845.0,64,,,,2013-07-21-075730.gpx
+2013-07-19 18:40:13,0547ce8b-c7c8-40b6-bb71-feff6298c5be,Running,,1.72,8:33,4:58,12.07,40.0,6,,,,2013-07-19-184013.gpx
+2013-07-18 07:47:00,1eb83982-20b9-494a-954c-ed4152f01273,Running,,6.15,32:36,5:18,11.33,416.0,30,,,,2013-07-18-074700.gpx
+2013-07-07 08:43:13,f85d8da1-0d59-4b30-bc5a-fce86874b167,Running,,3.75,21:36,5:46,10.41,281.0,56,,,,2013-07-07-084313.gpx
+2013-07-06 07:15:00,8de39009-9e2e-44ce-ad83-eb129f896e61,Running,,3.75,21:12,5:39,10.61,280.0,55,,,,2013-07-06-071500.gpx
+2013-07-05 08:01:00,a1f6c8f6-f85f-454e-8c2a-6946404f9d2a,Running,,3.75,21:10,5:39,10.63,280.0,55,,,,2013-07-05-080100.gpx
+2013-07-03 07:20:00,e4d4f094-e32e-4518-8119-742667e662c4,Running,,3.75,21:55,5:51,10.26,281.0,55,,,,2013-07-03-072000.gpx
+2013-07-02 07:43:00,0e41b390-7317-4d7f-9e0e-b1db01825f31,Running,,3.75,21:40,5:47,10.38,281.0,55,,,,2013-07-02-074300.gpx
+2013-07-01 07:09:00,fbd264b0-ca3a-4af0-97a8-36ef4fd84db7,Running,,3.75,21:10,5:39,10.63,262.0,0,,,,
+2013-06-26 18:19:00,64178ec8-556d-4d95-a2b3-249441e33199,Running,,16.0,1:06:49,4:11,14.37,1101.0,0,,,,
+2013-06-22 08:05:20,3dbd78e7-44eb-4dd9-9bc6-f2c94ff1acc9,Running,,6.23,32:25,5:12,11.52,446.0,35,,,,2013-06-22-080520.gpx
+2013-06-15 08:44:27,6ada32e6-e73f-44a2-a4d5-bf6fd194986e,Running,,6.37,29:53,4:41,12.79,417.0,36,,,,2013-06-15-084427.gpx
+2013-06-12 18:21:40,d5ce4d6e-3607-4eab-bae6-35beb7c2f4da,Running,,16.59,1:09:29,4:11,14.32,963.0,77,,,,2013-06-12-182140.gpx
+2013-06-08 07:45:08,b144b295-a4a7-4065-b9f1-0cf24dcf378e,Running,,6.35,33:51,5:20,11.24,455.0,36,,,,2013-06-08-074508.gpx
+2013-06-03 07:04:59,d091607e-10a2-467a-93d6-f2c54d47a0d9,Walking,,1.33,11:59,9:03,6.63,76.0,5,,,,2013-06-03-070459.gpx
+2013-06-01 07:24:07,45431395-0b88-4709-9477-22ddc6333f71,Running,,12.57,1:06:30,5:18,11.33,898.0,67,,,,2013-06-01-072407.gpx
+2013-05-28 07:16:32,8f49ef93-f404-4abe-9925-adf9cd7a330e,Running,,6.5,24:17,3:44,16.05,361.0,30,,,,2013-05-28-071632.gpx
+2013-05-21 18:45:38,ff3611e3-eb35-4a44-bafa-4a8696fcf8aa,Running,,11.63,1:00:35,5:12,11.52,833.0,63,,,,2013-05-21-184538.gpx
+2013-05-11 08:06:42,4c1d24a6-fd63-46b3-8379-16fc55a506a9,Running,,6.15,31:38,5:08,11.67,420.0,32,,,,2013-05-11-080642.gpx
+2013-05-09 17:24:19,b22dfd09-a8df-4796-86e4-e4d00ca151f6,Cycling,,15.47,38:08,2:28,24.33,339.0,88,,,,2013-05-09-172419.gpx
+2013-05-09 09:20:00,17f6a5bb-983c-4207-8cbe-9e3fc66e99fb,Running,,6.53,40:00,6:08,9.79,430.0,0,,,,
+2013-05-05 14:26:37,4370c545-ff6f-4829-967d-b0dbd0b54798,Cycling,,20.3,1:06:29,3:17,18.31,498.0,97,,,,2013-05-05-142637.gpx
+2013-05-05 09:13:02,23fdde24-02f1-4e54-9e0a-f05db884dc5b,Running,,6.41,33:58,5:18,11.33,454.0,34,,,,2013-05-05-091302.gpx
+2013-05-04 08:47:51,1f942355-8c6a-4ef4-87fd-36a1e99f5a3f,Running,,3.15,16:35,5:16,11.38,225.0,16,,,,2013-05-04-084751.gpx
+2013-05-02 08:36:30,0b4fc38b-5a1d-4b1f-887c-e643f5cb5624,Running,,6.21,32:51,5:18,11.33,445.0,36,,,,2013-05-02-083630.gpx
+2013-05-01 08:14:59,911ec4f7-a0ec-4385-971d-d5c63fba60de,Running,,6.22,32:29,5:13,11.48,446.0,34,,,,2013-05-01-081459.gpx
+2013-04-29 18:48:30,f157d4ff-bbf3-47e9-b07c-58f692ef9e6f,Walking,,1.37,22:39,16:30,3.64,95.0,10,,,,2013-04-29-184830.gpx
+2013-04-29 13:10:14,313fbef4-deeb-4da5-b1b2-ecee74cba0f6,Walking,,3.83,38:30,10:04,5.96,255.0,25,,,,2013-04-29-131014.gpx
+2013-04-28 17:26:52,ea228333-1dc2-4798-90e6-d4b7aa93545c,Cycling,,13.87,48:27,3:30,17.17,341.0,133,,,,2013-04-28-172652.gpx
+2013-04-28 10:56:47,7f6060d2-2bed-4370-8baa-c2c74164ec2d,Walking,,1.32,13:48,10:30,5.72,67.0,5,,,,2013-04-28-105647.gpx
+2013-04-28 09:02:23,cec17320-57d8-411b-af44-10f6f769b89b,Running,,3.12,16:36,5:19,11.28,224.0,17,,,,2013-04-28-090223.gpx
+2013-04-24 18:31:02,75670af6-0184-4f69-9df5-40020f998a66,Running,,6.25,29:29,4:43,12.71,399.0,33,,,,2013-04-24-183102.gpx
+2013-04-18 21:48:44,71e7c467-b916-4c78-870e-bdd2d1d2d03b,Walking,,1.5,24:41,16:28,3.64,86.0,9,,,,2013-04-18-214844.gpx
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+2013-02-22 19:23:38,df659987-77a5-4c7b-8633-74dbb15d3390,Running,,6.32,35:58,5:41,10.55,454.0,35,,,,2013-02-22-192338.gpx
+2013-02-20 18:43:56,d55c811c-8a0e-4222-857c-58552d95ccc8,Running,,9.59,52:35,5:29,10.94,687.0,52,,,,2013-02-20-184356.gpx
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+2013-01-15 18:40:08,7c512661-6a01-4ef0-80dd-b8119d5f8a59,Running,,6.72,32:42,4:52,12.34,398.0,32,,,,2013-01-15-184008.gpx
+2013-01-12 08:39:55,3a08d9a1-fc17-4002-80c1-419700be1e22,Running,,3.09,20:07,6:31,9.21,201.0,13,,,,2013-01-12-083955.gpx
+2013-01-02 18:35:11,57ff5696-7610-4296-903a-88c63c207603,Running,,5.47,31:24,5:45,10.44,392.0,28,,,,2013-01-02-183511.gpx
+2012-12-22 08:29:13,d7cceab2-22af-4472-9dea-5380ebe7ecd4,Running,,3.05,18:44,6:09,9.76,216.0,17,,,,2012-12-22-082913.gpx
+2012-12-15 09:43:53,d29db938-2212-4ea2-9f5c-4ddc42a14510,Running,,3.14,20:08,6:25,9.36,227.0,18,,,,2012-12-15-094353.gpx
+2012-12-08 08:37:38,f7eca580-962a-41b8-90ad-e7369b2f2dd7,Running,,3.21,18:50,5:53,10.21,228.0,18,,,,2012-12-08-083738.gpx
+2012-12-04 18:52:39,9da5a18d-925d-41d4-9468-c4a1e3b29185,Running,,6.63,32:05,4:50,12.39,406.0,30,,,,2012-12-04-185239.gpx
+2012-12-01 08:21:18,b5f841a8-d2e1-47b2-adc5-14bf65172db3,Running,,3.31,18:07,5:28,10.97,230.0,18,,,,2012-12-01-082118.gpx
+2012-11-28 18:58:02,cca72ec6-e859-453e-b099-0425b5372062,Running,,6.28,37:29,5:58,10.04,450.0,35,,,,2012-11-28-185802.gpx
+2012-11-24 08:57:47,0cec5506-32bd-423c-b651-b76718eaba89,Running,,2.65,14:07,5:20,11.27,186.0,15,,,,2012-11-24-085747.gpx
+2012-11-18 09:51:18,fc685fa0-d2b5-4984-b336-af3e578f456d,Running,,3.18,17:13,5:25,11.09,228.0,18,,,,2012-11-18-095118.gpx
+2012-11-10 08:37:49,6660d9fc-c010-4de8-8d42-8ec5857a2493,Running,,2.85,16:52,5:55,10.14,205.0,16,,,,2012-11-10-083749.gpx
+2012-11-07 18:54:35,9a0c4163-980c-45a3-a553-1c48503b0cbc,Running,,4.79,27:16,5:42,10.53,340.0,23,,,,2012-11-07-185435.gpx
+2012-11-04 18:59:06,3909e197-5ff0-4f6d-8508-358d2da68249,Walking,,1.22,12:05,9:54,6.07,67.0,10,,,,2012-11-04-185906.gpx
+2012-10-23 18:43:56,1387c891-e974-4c9f-9e52-3d211fb12a68,Running,,5.2,29:26,5:40,10.59,371.0,27,,,,2012-10-23-184356.gpx
+2012-10-20 08:37:33,e91aba7a-6bc5-418a-98f4-e6b23d80e7e5,Running,,3.18,19:28,6:07,9.8,228.0,18,,,,2012-10-20-083733.gpx
+2012-10-18 18:39:07,9beb9122-ddc7-4e0b-ac94-7c3064fcf669,Running,,3.7,19:17,5:13,11.51,264.0,20,,,,2012-10-18-183907.gpx
+2012-10-16 20:27:31,20e33458-ae07-4b47-92d8-5d565666216a,Running,,1.28,13:17,10:24,5.77,69.0,9,,,,2012-10-16-202731.gpx
+2012-09-22 08:56:08,07b5c213-ee70-4499-ad44-fd8cfe3f2fb5,Running,,3.16,16:26,5:13,11.52,225.0,18,,,,2012-09-22-085608.gpx
+2012-09-15 08:05:15,94a6d776-4746-4fab-9923-e8d443888e9c,Running,,3.11,16:58,5:28,10.98,223.0,18,,,,2012-09-15-080515.gpx
+2012-09-08 08:35:02,730f5507-59cc-43e6-b696-387f0946c57e,Running,,3.27,15:55,4:52,12.32,231.0,15,,,,2012-09-08-083502.gpx
+2012-09-04 19:12:17,9a6868f1-a41c-435e-9775-d7a803aa61ad,Running,,6.26,32:35,5:12,11.53,455.0,34,,,,2012-09-04-191217.gpx
+2012-09-03 15:25:22,a5be0bd3-03ae-430e-bcc8-c4a02d0a921e,Walking,,2.49,26:58,10:51,5.53,189.0,14,,,,2012-09-03-152522.gpx
+2012-09-03 13:20:56,1295cefc-d712-4e02-8915-61cf3dc4a4b8,Walking,,4.29,39:25,9:12,6.53,316.0,112,,,,2012-09-03-132056.gpx
+2012-09-02 08:41:31,4a9e2e1b-3a98-4630-8a89-3632aea5559a,Running,,3.14,16:16,5:11,11.56,230.0,18,,,,2012-09-02-084131.gpx
+2012-08-30 07:10:21,3067c059-dd1a-4df7-90b7-5e30b9d7e355,Walking,,1.43,15:12,10:40,5.63,77.0,7,,,I was feeling sick,2012-08-30-071021.gpx
+2012-08-29 18:19:26,bad065da-c772-4acc-a986-bc60aafa9473,Walking,,1.34,13:14,9:55,6.05,82.0,16,,,,2012-08-29-181926.gpx
+2012-08-29 08:21:43,5c8520ac-6d45-4c1d-b0a9-61e9c689c1fd,Walking,,1.48,14:48,10:02,5.98,89.0,6,,,,2012-08-29-082143.gpx
+2012-08-28 19:43:52,159e2b06-5808-4204-9d86-013132be67af,Walking,,1.46,13:24,9:12,6.52,87.0,13,,,,2012-08-28-194352.gpx
+2012-08-28 07:06:57,f5218490-a372-44c8-bb20-de3b91984cbe,Walking,,1.57,13:39,8:41,6.91,926743.0,7,,,,2012-08-28-070657.gpx
+2012-08-24 12:59:42,018f66a7-da5e-4985-a8fe-725a33317c87,Walking,,1.48,17:56,12:09,4.94,942192.0,12,,,,2012-08-24-125942.gpx
+2012-08-24 10:12:16,7acec95a-d63d-435d-837c-7befb352f500,Walking,,1.49,13:43,9:14,6.49,924486.0,9,,,,2012-08-24-101216.gpx
+2012-08-24 08:13:12,f790bdb2-b921-4018-bd39-d59d870c5847,Running,,3.15,16:00,5:05,11.82,2288868.0,17,,,,2012-08-24-081312.gpx
+2012-08-22 18:53:54,706d4d6b-767d-40aa-81c9-e5e85a102051,Running,,5.69,31:08,5:29,10.95,4072685.0,32,,,,2012-08-22-185354.gpx
diff --git a/Intermediate/Logistic Regression.ipynb b/Intermediate/Logistic Regression.ipynb
new file mode 100644
index 0000000..124e2f3
--- /dev/null
+++ b/Intermediate/Logistic Regression.ipynb	
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing libraries\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import sklearn \n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sample code number</th>\n",
+       "      <th>Clump Thickness</th>\n",
+       "      <th>Uniformity of Cell Size</th>\n",
+       "      <th>Uniformity of Cell Shape</th>\n",
+       "      <th>Marginal Adhesion</th>\n",
+       "      <th>Single Epithelial Cell Size</th>\n",
+       "      <th>Bare Nuclei</th>\n",
+       "      <th>Bland Chromatin</th>\n",
+       "      <th>Normal Nucleoli</th>\n",
+       "      <th>Mitoses</th>\n",
+       "      <th>Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1000025</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1002945</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1015425</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1016277</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1017023</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sample code number  Clump Thickness  Uniformity of Cell Size  \\\n",
+       "0             1000025                5                        1   \n",
+       "1             1002945                5                        4   \n",
+       "2             1015425                3                        1   \n",
+       "3             1016277                6                        8   \n",
+       "4             1017023                4                        1   \n",
+       "\n",
+       "   Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
+       "0                         1                  1                            2   \n",
+       "1                         4                  5                            7   \n",
+       "2                         1                  1                            2   \n",
+       "3                         8                  1                            3   \n",
+       "4                         1                  3                            2   \n",
+       "\n",
+       "   Bare Nuclei  Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
+       "0            1                3                1        1      2  \n",
+       "1           10                3                2        1      2  \n",
+       "2            2                3                1        1      2  \n",
+       "3            4                3                7        1      2  \n",
+       "4            1                3                1        1      2  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#importing dataset using pandas\n",
+    "dataset = pd.read_csv(r'E:\\Python\\data\\breast_cancer.csv')\n",
+    "dataset.shape\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size',\n",
+      "       'Uniformity of Cell Shape', 'Marginal Adhesion',\n",
+      "       'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin',\n",
+      "       'Normal Nucleoli', 'Mitoses', 'Class'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dataset.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#independet data \n",
+    "X = dataset.iloc[:, 1:-1].values\n",
+    "#dependent data \n",
+    "y = dataset.iloc[:, -1].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#splitting of dataset in train and test data \n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train,x_test,y_train,y_test = train_test_split(X,y,test_size = 0.2 , random_state = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "e:\\python\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
+       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
+       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
+       "                   warm_start=False)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "Lr = LogisticRegression()\n",
+    "Lr.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "4 4\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "4 4\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "4 4\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "4 4\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "4 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "4 2\n",
+      "4 4\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "4 4\n",
+      "2 4\n",
+      "2 4\n",
+      "4 2\n",
+      "4 4\n",
+      "4 4\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 4\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "2 4\n",
+      "2 2\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "4 4\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n",
+      "2 2\n",
+      "4 4\n",
+      "2 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "Y_pred = Lr.predict(x_test)\n",
+    "test_list = [Y_pred , y_test]\n",
+    "\n",
+    "for x, y in zip(*test_list): \n",
+    "    print(x, y) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.948905109489051\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = Lr.score(x_test , y_test)\n",
+    "print(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[84  3]\n",
+      " [ 4 46]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "cm = metrics.confusion_matrix(y_test, Y_pred)\n",
+    "print(cm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
+    "plt.ylabel('Actual label');\n",
+    "plt.xlabel('Predicted label');\n",
+    "all_sample_title = 'Accuracy Score: {0}'.format(score)\n",
+    "plt.title(all_sample_title, size = 15);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}