ML_Delivery_PRJ_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab433c85",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "795190df",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/mdami/OneDrive/Documents/Project - 1&2_ Simple_Reg/Project - 1&2_ Simple_Reg/delivery_time.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2f646c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Delivery Time  Sorting Time\n",
      "0           21.00            10\n",
      "1           13.50             4\n",
      "2           19.75             6\n",
      "3           24.00             9\n",
      "4           29.00            10\n",
      "5           15.35             6\n",
      "6           19.00             7\n",
      "7            9.50             3\n",
      "8           17.90            10\n",
      "9           18.75             9\n",
      "10          19.83             8\n",
      "11          10.75             4\n",
      "12          16.68             7\n",
      "13          11.50             3\n",
      "14          12.03             3\n",
      "15          14.88             4\n",
      "16          13.75             6\n",
      "17          18.11             7\n",
      "18           8.00             2\n",
      "19          17.83             7\n",
      "20          21.50             5\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e9f5587",
   "metadata": {},
   "outputs": [],
   "source": [
    "# exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4e96ac2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Delivery Time', 'Sorting Time'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc35569e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.describe of     Delivery Time  Sorting Time\n",
      "0           21.00            10\n",
      "1           13.50             4\n",
      "2           19.75             6\n",
      "3           24.00             9\n",
      "4           29.00            10\n",
      "5           15.35             6\n",
      "6           19.00             7\n",
      "7            9.50             3\n",
      "8           17.90            10\n",
      "9           18.75             9\n",
      "10          19.83             8\n",
      "11          10.75             4\n",
      "12          16.68             7\n",
      "13          11.50             3\n",
      "14          12.03             3\n",
      "15          14.88             4\n",
      "16          13.75             6\n",
      "17          18.11             7\n",
      "18           8.00             2\n",
      "19          17.83             7\n",
      "20          21.50             5>\n"
     ]
    }
   ],
   "source": [
    "print(data.describe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0004a7cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9eb4fa42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delivery Time    0\n",
      "Sorting Time     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "19a20449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delivery Time    float64\n",
      "Sorting Time       int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(data.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b78e377",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows and Columns- (21, 2)\n"
     ]
    }
   ],
   "source": [
    "print(\"Rows and Columns-\" , data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc5dfbcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Delivery Time  Sorting Time\n",
      "0          21.00            10\n",
      "1          13.50             4\n",
      "2          19.75             6\n",
      "3          24.00             9\n",
      "4          29.00            10\n"
     ]
    }
   ],
   "source": [
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2766bb4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sorting Time', ylabel='Delivery Time'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,4))\n",
    "sns.barplot(x= 'Sorting Time', y= 'Delivery Time', estimator= 'sum', errorbar= None, data=data )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0439e52a",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (539039906.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[23], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    Using matplotlib.pyplot.imsave()\u001b[0m\n\u001b[1;37m          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Usingmatplotlib.pyplot.imsave()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "28944f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Outliers')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,4))\n",
    "sns.boxplot(x=data['Delivery Time'])\n",
    "plt.title('Outliers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c5059e56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatter Plot')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,3))\n",
    "sns.scatterplot(x='Sorting Time', y='Delivery Time', data=data)\n",
    "plt.title('Scatter Plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4a6ced2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation Matrix')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c= data[['Delivery Time','Sorting Time']].corr()\n",
    "sns.heatmap(c, annot= True, cmap= 'RdBu')\n",
    "plt.title('Correlation Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b65ecc2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mdami\\AppData\\Local\\Temp\\ipykernel_70716\\3282645187.py:2: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=('ci', False)` for the same effect.\n",
      "\n",
      "  sns.lineplot(y='Delivery Time', x= 'Sorting Time', ci= False, data=data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sorting Time', ylabel='Delivery Time'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,4))\n",
    "sns.lineplot(y='Delivery Time', x= 'Sorting Time', ci= False, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c6edf68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Missing Values')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull(), cmap= 'viridis')\n",
    "plt.title('Missing Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "00dee0b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= data['Sorting Time'].values.reshape(-1,1)\n",
    "y= data['Delivery Time'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d2a50ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[10],\n",
       "        [ 4],\n",
       "        [ 6],\n",
       "        [ 9],\n",
       "        [10],\n",
       "        [ 6],\n",
       "        [ 7],\n",
       "        [ 3],\n",
       "        [10],\n",
       "        [ 9],\n",
       "        [ 8],\n",
       "        [ 4],\n",
       "        [ 7],\n",
       "        [ 3],\n",
       "        [ 3],\n",
       "        [ 4],\n",
       "        [ 6],\n",
       "        [ 7],\n",
       "        [ 2],\n",
       "        [ 7],\n",
       "        [ 5]], dtype=int64),\n",
       " array([[21.  ],\n",
       "        [13.5 ],\n",
       "        [19.75],\n",
       "        [24.  ],\n",
       "        [29.  ],\n",
       "        [15.35],\n",
       "        [19.  ],\n",
       "        [ 9.5 ],\n",
       "        [17.9 ],\n",
       "        [18.75],\n",
       "        [19.83],\n",
       "        [10.75],\n",
       "        [16.68],\n",
       "        [11.5 ],\n",
       "        [12.03],\n",
       "        [14.88],\n",
       "        [13.75],\n",
       "        [18.11],\n",
       "        [ 8.  ],\n",
       "        [17.83],\n",
       "        [21.5 ]]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c130b8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting of data into training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "19fd6d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "58953ee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test= train_test_split(x,y, test_size= 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1de7d621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16, 1), (5, 1))"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "25162ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16, 1), (5, 1))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b84cad7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a87271f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model= LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3b281a90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model training is completed\n"
     ]
    }
   ],
   "source": [
    "model_train= model.fit(x_train, y_train)\n",
    "print(\"The model training is completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb1d8e1b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3a5211fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# validating data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2aba0665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.48162077],\n",
       "       [12.22051141],\n",
       "       [10.80014162],\n",
       "       [20.74273013],\n",
       "       [12.22051141]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred= model.predict(x_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ac0aec2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bf8114a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(r2_score(y_test,pred),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "bf1be118",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ACCURACY OF MODEL- 81 % "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "486d8d1a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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