Simple linear Task 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Hours</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Scores</td>\n",
       "      <td>21.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0     1     2     3     4\n",
       "Hours    2.5   5.1   3.2   8.5   3.5\n",
       "Scores  21.0  47.0  27.0  75.0  30.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the data\n",
    "link = \"http://bit.ly/w-data\"\n",
    "df = pd.read_csv(link)\n",
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      "Hours     25 non-null float64\n",
      "Scores    25 non-null int64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "on avg student study 5 hr to score 51.48"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "# Plotting the distribution of scores\n",
    "df.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Traning the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting the x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values  \n",
    "y = df.iloc[:, 1].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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, \n",
    "                            test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising the Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "# 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": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test set prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.88414476, 33.73226078, 75.357018  , 26.79480124, 60.49103328])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 27, 69, 30, 62])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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, regressor.predict(X_train), color = 'black')\n",
    "plt.title('Score vs Hours of Study (Test set)')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[16.88414476 20.        ]\n",
      " [33.73226078 27.        ]\n",
      " [75.357018   69.        ]\n",
      " [26.79480124 30.        ]\n",
      " [60.49103328 62.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error,r2_score\n",
    "r2_s =r2_score(y_test, y_pred)\n",
    "mae = mean_absolute_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.183859899002975"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9454906892105356"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "r2 score the 94% which mean 94% model fit the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What will be the predicted score if a student study for 9.25 hrs in a day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([93.69173249])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_score = regressor.predict([[9.25]])\n",
    "predicted_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The predicated score is 93.69%"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}