Module 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yvRFuFBLrsac"
   },
   "source": [
    "# Machine Learning Basics\n",
    "In this module, you'll be implementing a simple Linear Regressor and Logistic Regressor. You will be using the Salary Data for the tasks in this module. <br> <br>\n",
    "**Pipeline:**\n",
    "* Acquiring the data - done\n",
    "* Handling files and formats - done\n",
    "* Data Analysis - done\n",
    "* Prediction\n",
    "* Analysing results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AwvgLLICtyt_"
   },
   "source": [
    "## Imports\n",
    "You may require NumPy, pandas, matplotlib and scikit-learn for this module. Do not, however, use the inbuilt Linear and Logistic Regressors from scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import csv\n",
    "df=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yE5Sz6nKvjTS"
   },
   "source": [
    "## Dataset\n",
    "You can load the dataset and perform any dataset related operations here. Split the data into training and testing sets. Do this separately for the regression and classification problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"/Users/msml/Vamsi/SF/MLBasics-master/Data/SalaryData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.iloc[:, :-1].values\n",
    "y=data.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test= train_test_split(x, y, test_size=1/3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VienPTZA1ZEr"
   },
   "source": [
    "## Task 1a - Linear Regressor\n",
    "Code your own Linear Regressor here, and fit it to your training data. You will be predicting salary based on years of experience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regression=LinearRegression()\n",
    "regression.fit(x_train, y_train)\n",
    "y_predic=regression.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431., 112635.,\n",
       "        55794.,  83088., 101302.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40835.10590871, 123079.39940819,  65134.55626083,  63265.36777221,\n",
       "       115602.64545369, 108125.8914992 , 116537.23969801,  64199.96201652,\n",
       "        76349.68719258, 100649.1375447 ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "plt.scatter(x_train, y_train, color='red')\n",
    "plt.plot(x_train, regression.predict(x_train), color='green')\n",
    "plt.title('Salary vs Experience')\n",
    "plt.xlabel('Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "plt.scatter(x_test, y_test, color='red')\n",
    "plt.plot(x_train, regression.predict(x_train), color='green')\n",
    "plt.title('Salary vs Experience')\n",
    "plt.xlabel('Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9876.972964280323 22914.688812206812\n"
     ]
    },
    {
     "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": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "data=pd.read_csv(\"/Users/msml/Vamsi/SF/MLBasics-master/Data/SalaryData.csv\")\n",
    "X = data.iloc[:, 0]\n",
    "Y = data.iloc[:, 1]\n",
    "m = 0\n",
    "c = 0\n",
    "L = 0.001  \n",
    "n = float(len(X))\n",
    "for i in range(10000): \n",
    "    Y_pred = m*X + c  \n",
    "    D_m = (-1/n) * sum(X * (Y - Y_pred))  \n",
    "    D_c = (-1/n) * sum(Y - Y_pred)  \n",
    "    m = m - L * D_m  \n",
    "    c = c - L * D_c  \n",
    "    \n",
    "print (m, c)\n",
    "Y_pred = m*X + c\n",
    "plt.scatter(X, Y) \n",
    "plt.plot([min(X), max(X)], [min(Y_pred), max(Y_pred)], color='red')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1b - Logistic Regression\n",
    "Code your own Logistic Regressor here, and fit it to your training data. You will first have to create a column, 'Salary<60000', which contains '1' if salary is less than 60000 and '0' otherwise. This is your target variable, which you will aim to predict based on years of experience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Salary&lt;60000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary Salary<60000\n",
       "0               1.1   39343.0            1\n",
       "1               1.3   46205.0            1\n",
       "2               1.5   37731.0            1\n",
       "3               2.0   43525.0            1\n",
       "4               2.2   39891.0            1\n",
       "5               2.9   56642.0            1\n",
       "6               3.0   60150.0            0\n",
       "7               3.2   54445.0            1\n",
       "8               3.2   64445.0            0\n",
       "9               3.7   57189.0            1\n",
       "10              3.9   63218.0            0\n",
       "11              4.0   55794.0            1\n",
       "12              4.0   56957.0            1\n",
       "13              4.1   57081.0            1\n",
       "14              4.5   61111.0            0\n",
       "15              4.9   67938.0            0\n",
       "16              5.1   66029.0            0\n",
       "17              5.3   83088.0            0\n",
       "18              5.9   81363.0            0\n",
       "19              6.0   93940.0            0\n",
       "20              6.8   91738.0            0\n",
       "21              7.1   98273.0            0\n",
       "22              7.9  101302.0            0\n",
       "23              8.2  113812.0            0\n",
       "24              8.7  109431.0            0\n",
       "25              9.0  105582.0            0\n",
       "26              9.5  116969.0            0\n",
       "27              9.6  112635.0            0\n",
       "28             10.3  122391.0            0\n",
       "29             10.5  121872.0            0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sala= pd.Series([])\n",
    "data=pd.read_csv(\"/Users/msml/Vamsi/SF/MLBasics-master/Data/SalaryData.csv\")\n",
    "sal=data['Salary']\n",
    "for i in range(len(data)):\n",
    "    if data[\"Salary\"][i]<60000:\n",
    "        Sala[i]=\"1\"\n",
    "    elif data[\"Salary\"][i]>60000:\n",
    "        Sala[i]=\"0\"\n",
    "data.insert(2, \"Salary<60000\", Sala) \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.47739755668809286 -0.07600274077188186\n",
      "0    -0.601140\n",
      "1    -0.696620\n",
      "2    -0.792099\n",
      "3    -1.030798\n",
      "4    -1.126277\n",
      "5    -1.460456\n",
      "6    -1.508195\n",
      "7    -1.603675\n",
      "8    -1.603675\n",
      "9    -1.842374\n",
      "10   -1.937853\n",
      "11   -1.985593\n",
      "12   -1.985593\n",
      "13   -2.033333\n",
      "14   -2.224292\n",
      "15   -2.415251\n",
      "16   -2.510730\n",
      "17   -2.606210\n",
      "18   -2.892648\n",
      "19   -2.940388\n",
      "20   -3.322306\n",
      "21   -3.465525\n",
      "22   -3.847443\n",
      "23   -3.990663\n",
      "24   -4.229361\n",
      "25   -4.372581\n",
      "26   -4.611280\n",
      "27   -4.659019\n",
      "28   -4.993198\n",
      "29   -5.088677\n",
      "Name: YearsExperience, dtype: float64\n",
      "0     0.354083\n",
      "1     0.332562\n",
      "2     0.311718\n",
      "3     0.262929\n",
      "4     0.244849\n",
      "5     0.188398\n",
      "6     0.181206\n",
      "7     0.167469\n",
      "8     0.167469\n",
      "9     0.136771\n",
      "10    0.125884\n",
      "11    0.120724\n",
      "12    0.120724\n",
      "13    0.115747\n",
      "14    0.097590\n",
      "15    0.082017\n",
      "16    0.075109\n",
      "17    0.068740\n",
      "18    0.052518\n",
      "19    0.050193\n",
      "20    0.034814\n",
      "21    0.030309\n",
      "22    0.020889\n",
      "23    0.018152\n",
      "24    0.014353\n",
      "25    0.012461\n",
      "26    0.009841\n",
      "27    0.009387\n",
      "28    0.006738\n",
      "29    0.006128\n",
      "Name: YearsExperience, dtype: float64\n",
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    },
    {
     "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": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "data=pd.read_csv(\"/Users/msml/Vamsi/SF/MLBasics-master/Data/SalaryData.csv\")\n",
    "X = data.iloc[:, 0]\n",
    "Y = data.iloc[:, 1]\n",
    "m = 0\n",
    "c = 0\n",
    "L = 0.000000001  \n",
    "n = float(len(X))\n",
    "for i in range(1000): \n",
    "    Y_pred = m*X + c\n",
    "    z=1/(1 + np.exp(-Y_pred))\n",
    "    D_m = (-1/n) * sum(X * (z-Y))  \n",
    "    D_c = (-1/n) * sum(z-Y)  \n",
    "    m = m - L * D_m  \n",
    "    c = c - L * D_c\n",
    "print (m, c)\n",
    "Y_pred = m*X + c\n",
    "print(Y_pred)\n",
    "z=1/(1 + np.exp(-Y_pred))\n",
    "print(z)\n",
    "y_cls = [1 if i>0.1 else 0 for i in z ]\n",
    "print(np.array(y_cls))\n",
    "plt.scatter(X,y_cls) \n",
    "plt.plot(X,y_cls)\n",
    "plt.xlabel('Experience')\n",
    "plt.ylabel('Sigmoidx')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vaCu6RS52qYf"
   },
   "source": [
    "## Task 2 - Results\n",
    "Analyse the quality of the ML models you built using metrics such as R2, MAE and RMSE for the Linear Regressor, and Accuracy for the Logistic Regressor. Evaluate their performance on the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Untitled15.ipynb",
   "provenance": [],
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}