linear_regression.ipynb

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   "source": [
    "# Musadique Hussain Magsi \n",
    "\n",
    "## Graduate Rotational Internship program Sparks Foundation-Data Science and Business Analytics\n",
    "\n",
    "### Task # 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1- Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2- Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "      <td>86</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\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"Data.xlsx\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
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       "    <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",
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      "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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3- Splitting dataset into training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[\"Hours\"]]\n",
    "Y = df[[\"Scores\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Hours</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
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       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
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       "   Hours\n",
       "0    2.5\n",
       "1    5.1\n",
       "2    3.2\n",
       "3    8.5\n",
       "4    3.5"
      ]
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     "metadata": {},
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   "source": [
    "X.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
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      "text/plain": [
       "   Scores\n",
       "0      21\n",
       "1      47\n",
       "2      27\n",
       "3      75\n",
       "4      30"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "Y.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imported library and split data\n",
    "\n",
    "X_train,X_test ,Y_train ,Y_test  = train_test_split(X,Y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4- Fit Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model = LinearRegression().fit(X_train, Y_train)\n",
    "model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ploting for Regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(X_train, Y_train)\n",
    "plt.plot(X_train, model.predict(X_train), color=\"Green\")\n",
    "plt.xlabel(\"Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.title(\"Train Plot\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(X_test, Y_test)\n",
    "plt.plot(X_test, model.predict(X_test), color=\"Green\")\n",
    "plt.xlabel(\"Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.title(\"Test Plot\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6- Testing or Evaluating your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for Training Data =  0.9515510725211552\n",
      "Score for Testing Data =  0.9454906892105356\n"
     ]
    }
   ],
   "source": [
    "# Model fitness\n",
    "print(\"Score for Training Data = \", model.score(X_train, Y_train))\n",
    "print(\"Score for Testing Data = \", model.score(X_test, Y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7- Prediction of unknown values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[93.69173249]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What will be predicted score if a student studies for 9.25 hours/day?\n",
    "model.predict([[9.25]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score =  95.15510725211553\n",
      "Testing Score =  94.54906892105356\n"
     ]
    }
   ],
   "source": [
    "# Regression score\n",
    "print(\"Training Score = \", model.score(X_train, Y_train)*100)\n",
    "print(\"Testing Score = \", model.score(X_test, Y_test)*100)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('Magsi')",
   "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": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "0a45be76c3efba00f5ef4d94356778e425f01065810a3419d48868e094693cfc"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}