1. ANN Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mohammed Yusuf Khan\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from statsmodels.api import OLS\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/getmykhan/toolstack/master/Datasets/Salary.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience   Salary\n",
       "0              1.1  39343.0\n",
       "1              1.3  46205.0\n",
       "2              1.5  37731.0\n",
       "3              2.0  43525.0\n",
       "4              2.2  39891.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = tf.keras.Sequential()\n",
    "model1.add(tf.keras.layers.Dense(1, input_shape=([1])))\n",
    "model1.compile(loss='mean_squared_error', optimizer=tf.optimizers.RMSprop(1), metrics=['mean_absolute_error', 'mean_squared_error'])\n",
    "model1_history = model1.fit(df['YearsExperience'], df['Salary'], epochs=12000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = tf.keras.Sequential()\n",
    "model2.add(tf.keras.layers.Dense(16,input_shape=([1]), activation=tf.nn.relu))\n",
    "model2.add(tf.keras.layers.Dense(32, activation=tf.nn.relu))\n",
    "model2.add(tf.keras.layers.Dense(1))\n",
    "model2.compile(loss='mean_squared_error', optimizer=tf.optimizers.RMSprop(0.01), metrics=['mean_absolute_error', 'mean_squared_error'])\n",
    "model2_history = model2.fit(df['YearsExperience'], df['Salary'], epochs=1000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = OLS(df['Salary'], df['YearsExperience'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = m.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Salary</td>      <th>  R-squared:         </th> <td>   0.973</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.972</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1048.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 23 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>2.56e-24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:57:01</td>     <th>  Log-Likelihood:    </th> <td> -327.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   656.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    29</td>      <th>  BIC:               </th> <td>   658.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>YearsExperience</th> <td> 1.325e+04</td> <td>  409.401</td> <td>   32.376</td> <td> 0.000</td> <td> 1.24e+04</td> <td> 1.41e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.610</td> <th>  Durbin-Watson:     </th> <td>   0.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.737</td> <th>  Jarque-Bera (JB):  </th> <td>   0.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.121</td> <th>  Prob(JB):          </th> <td>   0.715</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.308</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Salary   R-squared:                       0.973\n",
       "Model:                            OLS   Adj. R-squared:                  0.972\n",
       "Method:                 Least Squares   F-statistic:                     1048.\n",
       "Date:                Tue, 23 Apr 2019   Prob (F-statistic):           2.56e-24\n",
       "Time:                        16:57:01   Log-Likelihood:                -327.28\n",
       "No. Observations:                  30   AIC:                             656.6\n",
       "Df Residuals:                      29   BIC:                             658.0\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "YearsExperience  1.325e+04    409.401     32.376      0.000    1.24e+04    1.41e+04\n",
       "==============================================================================\n",
       "Omnibus:                        0.610   Durbin-Watson:                   0.323\n",
       "Prob(Omnibus):                  0.737   Jarque-Bera (JB):                0.671\n",
       "Skew:                          -0.121   Prob(JB):                        0.715\n",
       "Kurtosis:                       2.308   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13250.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.325e+04"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[11488.194]], dtype=float32), array([11979.816], dtype=float32)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.get_weights();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['y_pred'] = df['YearsExperience'] * 1.325e+04"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20751a85710>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,7))\n",
    "plt.scatter(df['YearsExperience'], df['Salary'], color='black')\n",
    "plt.plot(df['YearsExperience'], result.predict(df['YearsExperience']), label = 'OLS', color='red')\n",
    "plt.plot(df['YearsExperience'], [model1.predict([x])[0][0] for x in df['YearsExperience']], label ='Model1')\n",
    "plt.plot(df['YearsExperience'], [model2.predict([x])[0][0] for x in df['YearsExperience']], label ='Model2')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mohammed Yusuf Khan\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20751cbc2b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,7))\n",
    "sns.regplot(df['YearsExperience'], df['Salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ends here!"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}