Linear Regression - Gradient Descent with Continuous  Optimize Loss function.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics \n",
    "plt.rcParams['figure.figsize'] = (12.0, 9.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readfile_func(filename):\n",
    "    data = pd.read_csv(filename)\n",
    "    X = data.iloc[:,0].values.reshape(-1,1)\n",
    "    Y = data.iloc[:,1].values.reshape(-1,1)\n",
    "    plt.title('Salary VS Experience Data( Original Data )')\n",
    "    plt.scatter(X, Y)\n",
    "    plt.show()\n",
    "    return X, Y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_cost_func(X_train,Y_train,c,m):\n",
    "    cost = 0.0\n",
    "    for i in range(X_train.shape[0]):\n",
    "        cost += (c + m*X_train[i] - Y_train[i])**2 \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_gradient_func(X_train,Y_train,c,m):\n",
    "    d_m = 0\n",
    "    d_c = 0\n",
    "    size = X_train.shape[0]\n",
    "    for i in range(size):\n",
    "        d_c = d_c + (c + m*X_train[i] - Y_train[i])  #w.rt to c\n",
    "        d_m = d_m + ((c + m*X_train[i] - Y_train[i])*X_train[i])  #wrt to m\n",
    "    \n",
    "    d_c = d_c*(1.0/size)\n",
    "    d_m = d_m*(1.0/size)\n",
    "    return d_c, d_m\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_descent_func(X_train,Y_train,alpha =.00001, ep =.0001):\n",
    "    \n",
    "    \n",
    "    iteration = 0\n",
    "    converged = True\n",
    "\n",
    "    m =  np.random.randn(); \n",
    "    c = np.random.randn();\n",
    "    cost = calculate_cost_func(X_train,Y_train,c,m)\n",
    "    \n",
    "    while converged:\n",
    "        \n",
    "        d_c, d_m = log_gradient_func(X_train,Y_train,c,m)\n",
    "        temp0 = c - alpha * d_c    # finding new c and m from using gradient descent\n",
    "        temp1 = m - alpha * d_m\n",
    "        c = temp0  # updating c and m\n",
    "        m = temp1\n",
    "        e = calculate_cost_func(X_train,Y_train,c,m) # mean squared error to check if c and m are good fit \n",
    "        if abs(cost-e) <= ep:   # taking abs value to ignore the negative value\n",
    "            print('Converged, iterations: ', iteration, '!!!')\n",
    "            converged = False     \n",
    "        cost = e  \n",
    "        iteration = iteration + 1\n",
    "    return c,m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_func(X,c,m):\n",
    "    Y_pred = m*X + c\n",
    "    return Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bestFitLine(X,slope,coeff):\n",
    "    Y_pred = slope*X + coeff\n",
    "    return Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def buildModel_func(X,Y):\n",
    "    \n",
    "    # dividing my train and test size\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=1/3, random_state=0)\n",
    "    c,m = gradient_descent_func(X_train,Y_train)\n",
    "    \n",
    "    # plotting the results\n",
    "    plt.scatter(X_train,Y_train,color='red')\n",
    "    plt.plot(X_train,predict_func(X_train,c,m))\n",
    "    plt.title('Salary VS Experience (Training set)')\n",
    "    plt.xlabel('Year of Experience')\n",
    "    plt.ylabel('Salary')\n",
    "    plt.show()\n",
    "    \n",
    "    plt.scatter(X_test,Y_test,color='red')\n",
    "    plt.plot(X_test,predict_func(X_test,c,m))\n",
    "    plt.title('Salary VS Experience (Testing set)')\n",
    "    \n",
    "    plt.xlabel('Year of Experience')\n",
    "    plt.ylabel('Salary')\n",
    "    plt.show()\n",
    "    return c,m\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x190227ac978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, Y = readfile_func('salary_data.csv') #loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged, iterations:  4283814 !!!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19021883940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19030a04588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c,m = buildModel_func(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Years of Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39343</td>\n",
       "      <td>37094.854096</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46205</td>\n",
       "      <td>38964.118517</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37731</td>\n",
       "      <td>40833.382939</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43525</td>\n",
       "      <td>45506.543993</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39891</td>\n",
       "      <td>47375.808415</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56642</td>\n",
       "      <td>53918.233891</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60150</td>\n",
       "      <td>54852.866101</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>54445</td>\n",
       "      <td>56722.130523</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64445</td>\n",
       "      <td>56722.130523</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57189</td>\n",
       "      <td>61395.291577</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual     Predicted  Years of Experience\n",
       "0   39343  37094.854096                  1.1\n",
       "1   46205  38964.118517                  1.3\n",
       "2   37731  40833.382939                  1.5\n",
       "3   43525  45506.543993                  2.0\n",
       "4   39891  47375.808415                  2.2\n",
       "5   56642  53918.233891                  2.9\n",
       "6   60150  54852.866101                  3.0\n",
       "7   54445  56722.130523                  3.2\n",
       "8   64445  56722.130523                  3.2\n",
       "9   57189  61395.291577                  3.7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=bestFitLine(X,m,c)\n",
    "df_result = pd.DataFrame({'Years of Experience': X.flatten(),'Actual': Y.flatten(), \n",
    "                          'Predicted': y_pred.flatten()}).head(10)\n",
    "df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19030aa62e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df_result.head()\n",
    "df1.plot(kind='bar',figsize=(8,5))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.015', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.015', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4682.2534596690275\n",
      "Mean Squared Error: 31576438.518852543\n",
      "Root Mean Squared Error: 5619.291638529944\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(Y, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the value of root mean squared error is 5619, which is less than 10% of the mean value of the percentages of all the salary i.e. 76003. This means that our algorithm did a decent job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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