Task1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GzfdMfk10NE6"
   },
   "source": [
    "## **Data Science & Business Analytics**\n",
    "\n",
    "## **Task 1: Predicion using Supervised ML**\n",
    "\n",
    "### **Algorithm : Linear Regression**\n",
    "\n",
    "### **We are going to predict the percentage of marks that a student is expected to obtain by the number of hours they studied.**\n",
    "\n",
    "**To increase the performance of my model , i had used Standardization as explained below.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X6A8Hm86UUZ-"
   },
   "source": [
    "### Author: Goutham Pasumarthy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V9QN2ZxC38pB"
   },
   "outputs": [],
   "source": [
    "# Importing all libraries required in this notebook\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2534,
     "status": "ok",
     "timestamp": 1544113345787,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "LtU4YMEhqm9m",
    "outputId": "5b4b36af-1545-497e-a6dc-7658bab71dbc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully\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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30\n",
       "5    1.5      20\n",
       "6    9.2      88\n",
       "7    5.5      60\n",
       "8    8.3      81\n",
       "9    2.7      25"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "url = \"http://bit.ly/w-data\"\n",
    "s_data = pd.read_csv(url)\n",
    "print(\"Data imported successfully\")\n",
    "\n",
    "s_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observe that the range of values for the variables are different. Hours range from 0-24. and Scores from 0-100.\n",
    "#### In this case, there will be a bias as the variable with larger range will outweigh the other variable.\n",
    "#### Transforming the data to comparable scales can prevent this problem\n",
    "#### Although this task is simple and contains less data , it doesn't actually need this step. \n",
    "#### I'm gonna be using it anyways to try something new.\n",
    "\n",
    "\n",
    "**Now we are going to Standardize the data using StandardScaler**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.59872116e-16 1.33226763e-16]\n",
      "[1. 1.]\n",
      "       Hours    Scores\n",
      "0  -1.015328 -1.230223\n",
      "1   0.035569 -0.180820\n",
      "2  -0.732394 -0.988053\n",
      "3   1.409819  0.949306\n",
      "4  -0.611137 -0.866968\n",
      "5  -1.419519 -1.270585\n",
      "6   1.692753  1.474008\n",
      "7   0.197245  0.343881\n",
      "8   1.328981  1.191476\n",
      "9  -0.934490 -1.068777\n",
      "10  1.086466  1.352923\n",
      "11  0.358922  0.424605\n",
      "12 -0.206946 -0.422990\n",
      "13 -0.691975 -0.382629\n",
      "14 -1.581196 -1.391670\n",
      "15  1.571495  1.756539\n",
      "16 -1.015328 -0.866968\n",
      "17 -1.257843 -1.109138\n",
      "18  0.439760  0.626413\n",
      "19  0.965209  0.707136\n",
      "20 -0.934490 -0.866968\n",
      "21 -0.085689  0.101711\n",
      "22 -0.489880 -0.665160\n",
      "23  0.763113  0.989668\n",
      "24  1.126885  1.393284\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler() \n",
    "data_scaled = scaler.fit_transform(s_data)\n",
    "data_s = pd.DataFrame(data_scaled, columns = [\"Hours\",\"Scores\"])\n",
    "print(data_scaled.mean(axis=0))\n",
    "print(data_scaled.std(axis=0))\n",
    "print(data_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RHsPneuM4NgB"
   },
   "source": [
    "**Plotting original data vs Plotting scaled data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 718,
     "status": "ok",
     "timestamp": 1544113350499,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "qxYBZkhAqpn9",
    "outputId": "37264af1-786d-4e0c-a668-383264d1ddd1"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution of scores\n",
    "s_data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Original Data')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_s.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Scaled Data')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fiQaULio4Rzr"
   },
   "source": [
    "**From the graph above, we can clearly see that there is a positive linear relation between the number of hours studied and percentage of score.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WWtEr64M4jdz"
   },
   "source": [
    "### **Preparing the data**\n",
    "### In this part we are going to split the data into training and test sets\n",
    "The first step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LiJ5210e4tNX"
   },
   "outputs": [],
   "source": [
    "X = s_data.iloc[:, :-1].values  \n",
    "y = s_data.iloc[:, 1].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_s.iloc[:, :-1].values  \n",
    "y = data_s.iloc[:, 1].values  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Riz-ZiZ34fO4"
   },
   "source": [
    "Now that we have our attributes and labels, the next step is to split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "udFYso1M4BNw"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.2, random_state=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "a6WXptFU5CkC"
   },
   "source": [
    "### **Training the Algorithm**\n",
    "We have split our data into training and testing sets, and now is finally the time to train our algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 701,
     "status": "ok",
     "timestamp": 1544113358086,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "qddCuaS84fpK",
    "outputId": "befbd977-772c-4bd1-bb48-ee5dd6bae73c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "\n",
    "print(\"Training complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 985,
     "status": "ok",
     "timestamp": 1544113360867,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "J61NX2_2-px7",
    "outputId": "d20ec1fd-3e2d-4eae-84a2-a0df57d31009"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the regression line\n",
    "line = regressor.coef_*X+regressor.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JCQn-g4m5OK2"
   },
   "source": [
    "## **Making Predictions**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 698,
     "status": "ok",
     "timestamp": 1544113363729,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "Tt-Fmzu55EGM",
    "outputId": "46f1acf8-91ac-4984-cfbe-e614aa9ea849"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.4195194 ]\n",
      " [-0.7323944 ]\n",
      " [ 0.96520852]\n",
      " [-1.01532822]\n",
      " [ 0.35892176]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test) # Testing data - In Hours\n",
    "y_pred = regressor.predict(X_test) # Predicting the scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 753,
     "status": "ok",
     "timestamp": 1544113366918,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "6bmZUMZh5QLb",
    "outputId": "8ea11a9e-c1b7-4fab-ab62-4dcbd2c8607b"
   },
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.270585</td>\n",
       "      <td>-1.396346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.988053</td>\n",
       "      <td>-0.716328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.707136</td>\n",
       "      <td>0.963716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.866968</td>\n",
       "      <td>-0.996336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.424605</td>\n",
       "      <td>0.363700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual  Predicted\n",
       "0 -1.270585  -1.396346\n",
       "1 -0.988053  -0.716328\n",
       "2  0.707136   0.963716\n",
       "3 -0.866968  -0.996336\n",
       "4  0.424605   0.363700"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking input hours 9.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 862,
     "status": "ok",
     "timestamp": 1544113370494,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "KAFO8zbx-AH1",
    "outputId": "fcb3830f-3cda-4dcb-f122-84b71f101fae",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = [9.25]\n",
      "[[9.25, 0.16]]\n",
      "\n",
      "A temporary Data Frame For Standardizing Input with Score Yet to Be Calculated:\n",
      "   Hours  Scores\n",
      "0   9.25    0.16\n",
      "\n",
      " Standardized Value for the input hours : \n",
      " [ 1.71296219 -2.07136035]\n"
     ]
    }
   ],
   "source": [
    "hours = [[9.25]]\n",
    "#own_pred = regressor.predict(hours)\n",
    "print(\"No of Hours = {}\".format(hours[0]))\n",
    "hours[0].append(0.16)\n",
    "print(hours)\n",
    "x = pd.DataFrame(hours,[[0]],columns = [\"Hours\",\"Scores\"])\n",
    "print(\"\\nA temporary Data Frame For Standardizing Input with Score Yet to Be Calculated:\")\n",
    "print(x)\n",
    "temp = scaler.transform(x)[0]\n",
    "print(\"\\n Standardized Value for the input hours : \\n\" , temp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prediction of standardized score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Hours\t\tScore \n",
      "\n",
      "[ 9.25       93.69173249]\n",
      "\n",
      "Predicted Score = 93.69173248737539\n"
     ]
    }
   ],
   "source": [
    "temp[1] = regressor.predict([[temp[0]]])\n",
    "own_pred = scaler.inverse_transform(temp)\n",
    "print(\"\\n Hours\\t\\tScore \\n\")\n",
    "print(own_pred)\n",
    "print(\"\\nPredicted Score = {}\".format(own_pred[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **So, the predicted score for reading 9.25 hours in a day is 93.69**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0AAsPVA_6KmK"
   },
   "source": [
    "## **Evaluating the model**\n",
    "\n",
    "### **For evaluating the model, I used Mean Absolute Error.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 834,
     "status": "ok",
     "timestamp": 1544113374919,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "r5UOrRH-5VCQ",
    "outputId": "7b9ddcf1-2848-408f-d81f-7a60652c381e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.16886752752443157\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Comparsion**\n",
    "#### **If I did not use Standardization, the Mean Absolute Error would be around 4.25**\n",
    "#### **Now the Standardized Mean Absolute Error reduced to 0.16 after StandardScaling. It will scale well when compared to original MAE less than 4**\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Linear Regression.ipynb",
   "provenance": [
    {
     "file_id": "1wzD9Aa7cc7kRwyXq8DeJ8H56mJInOMZN",
     "timestamp": 1544113281508
    }
   ],
   "version": "0.3.2"
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}