TSF GRIP Task 1 Imteyaz.ipynb

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    "# TASK 1 - Data Science and Business Analytics Internship\n",
    "By MD Imteyaz Intern at The Sparks Foundation\n",
    "# Task : - Pridiction using Supervised ML\n",
    "Description : Pridict the percentage of on student based on the no. of study hours.This is a simple linear regression task as it involves just 2 variables. We used python programming language to pridicted score if a student studies for 9.25hrs/day\n",
    "\n",
    "\n",
    "# Importing libraries required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "998ec08b",
   "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",
       "    </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"
      ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#Importing the dataset\n",
    "data = 'http://bit.ly/w-data'\n",
    "df = pd.read_csv(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6b018f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "014529e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     float64\n",
       "Scores      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the datatypes of the columns\n",
    "df.dtypeshttps://github.com/Imteyaz5161/The_Sparks_Foundatation_internship.git  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "af16c61c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Hours', 'Scores'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Columns present in dataset\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "caeddcee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Some basic stats of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "faa60f33",
   "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",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b1cec3",
   "metadata": {},
   "source": [
    "# visualizing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "222c5577",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 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": "code",
   "execution_count": 36,
   "id": "236c6c20",
   "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",
       "      <th>Hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scores</th>\n",
       "      <td>0.976191</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours    Scores\n",
       "Hours   1.000000  0.976191\n",
       "Scores  0.976191  1.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "13eacbd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# From the graph above, we can clearly see that there is a positive linear relation between the number of hours studied and percentage of score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5873ec50",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mdimt\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Scores', ylabel='Density'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.distplot(df.Scores, norm_hist=True)\n",
    "#This graph depicts a normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "292d4f06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mdimt\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Scores'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(df.Scores)\n",
    "#This graph depicts that no outliers are present"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a2788e",
   "metadata": {},
   "source": [
    "# machine learning Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6887612d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 1) (5, 1) (20,) (5,)\n"
     ]
    }
   ],
   "source": [
    "#Splitting the dataset\n",
    "#The first step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs).\n",
    "X = df.Hours.values\n",
    "X=X.reshape(-1, 1)\n",
    "y = df.Scores\n",
    "from sklearn.model_selection import train_test_split  \n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y,test_size=0.2, random_state=0) \n",
    "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f09aee",
   "metadata": {},
   "source": [
    "\n",
    "For Training the Algorithm We have splitted our data into training and testing sets, and now is finally the time to train our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1951fde7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient is [9.91065648] and intercept is 2.018160041434683\n"
     ]
    }
   ],
   "source": [
    "#Applying Linear Regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "m=model.coef_\n",
    "c=model.intercept_\n",
    "print(f\"Coefficient is {m} and intercept is {c}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b44d8d74",
   "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 = model.coef_*X+model.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b83288ca",
   "metadata": {},
   "source": [
    "# making predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b978665f",
   "metadata": {},
   "source": [
    "Now that we have trained our algorithm, it's time to make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cc8e40ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([16.88414476, 33.73226078, 75.357018  , 26.79480124, 60.49103328])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(x_test) # Testing data - In Hours\n",
    "y_pred = model.predict(x_test) # Predicting the scores\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "aa1d8fae",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "5       20  16.884145\n",
       "2       27  33.732261\n",
       "19      69  75.357018\n",
       "16      30  26.794801\n",
       "11      62  60.491033"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparision\n",
    "comp = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1c1143f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE score: 21.5987693072174\n",
      "R2 score: 0.9454906892105356\n",
      "Mean Absolute Error: 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "#Evaluating the model\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import metrics  \n",
    "\n",
    "print('RMSE score:', mean_squared_error(y_test, y_pred))\n",
    "print('R2 score:', r2_score(y_test, y_pred))\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b9e8e55c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted score for a student who studies for 9.25 hours/day is [93.69173249]\n"
     ]
    }
   ],
   "source": [
    "# Final Step\n",
    "print(f\"The predicted score for a student who studies for 9.25 hours/day is {model.predict([[9.25]])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fce3dded",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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