 ML-Future-Sales-Prediction.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "165d4588",
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "        TV  Radio  Newspaper  Sales\n",
      "0    230.1   37.8       69.2   22.1\n",
      "1     44.5   39.3       45.1   10.4\n",
      "2     17.2   45.9       69.3   12.0\n",
      "3    151.5   41.3       58.5   16.5\n",
      "4    180.8   10.8       58.4   17.9\n",
      "..     ...    ...        ...    ...\n",
      "195   38.2    3.7       13.8    7.6\n",
      "196   94.2    4.9        8.1   14.0\n",
      "197  177.0    9.3        6.4   14.8\n",
      "198  283.6   42.0       66.2   25.5\n",
      "199  232.1    8.6        8.7   18.4\n",
      "\n",
      "[200 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"https://raw.githubusercontent.com/amankharwal/Website-data/master/advertising.csv\")\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1fb83ba3",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "0.9059011844150826\n",
      "[44.17037546]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17103/2627254823.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  x = np.array(data.drop([\"Sales\"], 1))\n"
     ]
    }
   ],
   "source": [
    "x = np.array(data.drop([\"Sales\"], 1))\n",
    "y = np.array(data[\"Sales\"])\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2,  random_state=42)\n",
    "\n",
    "#train the model to predict future sales\n",
    "model = LinearRegression()\n",
    "model.fit(xtrain, ytrain)\n",
    "print(model.score(xtest, ytest))\n",
    "\n",
    "#input values into the model according to the features we have used to train\n",
    "#features = [[TV, Radio, Newspaper]]\n",
    "features = np.array([[153.1, 307.8, 9.2]])\n",
    "print(model.predict(features))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "396c6f01",
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   "outputs": [],
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