my_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IRXrYEztwafH"
   },
   "source": [
    "# Data Driven Agriculture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dA9CBoY5wwJ1"
   },
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "egqg5sPWwi1V"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Bra_L0lw2kT"
   },
   "source": [
    "## Importing Dataset From csv File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "ALpMUsF0ke8B"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"model_data.csv\")\n",
    "x = dataset.iloc[:,:3]\n",
    "y = dataset.iloc[:,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PWh_THOMkifs",
    "outputId": "269ae051-8e2f-4142-efc9-dcaf60391192"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    year  population  wheat_surplus\n",
      "0   2020  1380004385          8.090\n",
      "1   2019  1366417754          7.490\n",
      "2   2018  1352642280          4.240\n",
      "3   2017  1338676785          2.830\n",
      "4   2016  1324517249         -4.940\n",
      "..   ...         ...            ...\n",
      "56  1964   488848135         -6.676\n",
      "57  1963   478825608         -5.271\n",
      "58  1962   469077190         -2.744\n",
      "59  1961   459642165         -2.895\n",
      "60  1960   450547679         -3.900\n",
      "\n",
      "[61 rows x 3 columns]\n",
      "0     99.50\n",
      "1     96.11\n",
      "2     95.63\n",
      "3     95.68\n",
      "4     97.23\n",
      "      ...  \n",
      "56    16.53\n",
      "57    16.05\n",
      "58    14.82\n",
      "59    13.89\n",
      "60    14.22\n",
      "Name: wheat_demands, Length: 61, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rx78ye16xILE"
   },
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KAnyTf7excLj"
   },
   "source": [
    "### Splitting data into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "T7R_th1wob8D"
   },
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vCNmTig_5D-i",
    "outputId": "980058b9-0b99-4b7c-e284-f075f82182c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    year  population  wheat_surplus\n",
      "26  1994   945601831          1.510\n",
      "35  1985   784360008          0.349\n",
      "60  1960   450547679         -3.900\n",
      "28  1992   909307016         -1.830\n",
      "11  2009  1217726215          2.530\n",
      "2   2018  1352642280          4.240\n",
      "34  1986   801975244          1.482\n",
      "59  1961   459642165         -2.895\n",
      "40  1980   698952844         -2.500\n",
      "22  1998  1019483581          2.640\n",
      "4   2016  1324517249         -4.940\n",
      "10  2010  1234281170         -0.960\n",
      "30  1990   873277798          2.250\n",
      "26    58.33\n",
      "35    43.72\n",
      "60    14.22\n",
      "28    57.52\n",
      "11    78.15\n",
      "2     95.63\n",
      "34    45.57\n",
      "59    13.89\n",
      "40    34.33\n",
      "22    63.71\n",
      "4     97.23\n",
      "10    81.76\n",
      "30    47.60\n",
      "Name: wheat_demands, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(x_test)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "efsi5sHG0Lck"
   },
   "source": [
    "### Seperating data for result visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "PhcDQViJ0U7J"
   },
   "outputs": [],
   "source": [
    "x_visual = np.array(x_test)\n",
    "y_visual = np.array(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_nWUUyQKxfG9"
   },
   "source": [
    "### Scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "BYEXm4xho-Rx"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "pQlUnTNf_sZj"
   },
   "outputs": [],
   "source": [
    "y_train = y_train.values.reshape(-1,1)\n",
    "y_test = y_test.values.reshape(-1,1)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_y = StandardScaler()\n",
    "y_train = sc_y.fit_transform(y_train)\n",
    "y_test = sc_y.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The vector of means for year, population, wheat surplus is: \n",
      "[ 1.98939583e+03  8.77977753e+08 -6.17166667e-01]\n",
      "The vector of std deviation for year, populaion, wheat_surplus is: \n",
      "[17.503261104878586, 290867828.9353266, 4.608428466468321]\n",
      "The mean of wheat demands is: \n",
      "[52.10958333]\n",
      "The std deviation of wheat demands is: \n",
      "[26.105036866980075]\n"
     ]
    }
   ],
   "source": [
    "print(\"The vector of means for year, population, wheat surplus is: \")\n",
    "print(sc.mean_)\n",
    "print(\"The vector of std deviation for year, populaion, wheat_surplus is: \")\n",
    "stddev = []\n",
    "for item in sc.var_:\n",
    "    stddev.append(item**0.5)\n",
    "print(stddev)\n",
    "\n",
    "print(\"The mean of wheat demands is: \")\n",
    "print(sc_y.mean_)\n",
    "print(\"The std deviation of wheat demands is: \")\n",
    "stddev = []\n",
    "for item in sc_y.var_:\n",
    "    stddev.append(item**0.5)\n",
    "print(stddev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TkVlNJqYx6iy"
   },
   "source": [
    "## Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fapwCi46BRHD",
    "outputId": "53acf5bb-bf68-4c03-f39d-7f13d81fc3e4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vip\\anaconda3\\envs\\farming\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "regressor = SVR(kernel = \"rbf\")\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "an86QNt_x-4r"
   },
   "source": [
    "## Testing the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.26304622  0.23249074  0.46158179]\n",
      " [-0.25114368 -0.32185665  0.20965209]\n",
      " [-1.67944894 -1.46949931 -0.71235419]\n",
      " [ 0.1487818   0.10770962 -0.26317721]\n",
      " [ 1.12002938  1.16805101  0.68291538]\n",
      " [ 1.63421927  1.63189077  1.05397463]\n",
      " [-0.19401147 -0.26129569  0.45550597]\n",
      " [-1.62231673 -1.43823258 -0.49427551]\n",
      " [-0.53680473 -0.61548543 -0.40856299]\n",
      " [ 0.49157506  0.48649529  0.70678469]\n",
      " [ 1.51995485  1.53519727 -0.93802765]\n",
      " [ 1.17716159  1.22496674 -0.07439268]\n",
      " [ 0.03451738 -0.01615839  0.62215714]]\n"
     ]
    }
   ],
   "source": [
    "print(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hRgDG4OvBztf",
    "outputId": "22cabdbb-eeb0-4d10-af5f-47127bc7c4f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([59.50709583, 45.2225854 , 18.4822097 , 58.24832624, 82.91627328,\n",
       "       94.69388916, 46.69197911, 19.14298189, 36.70135791, 64.41040363,\n",
       "       77.93186623, 83.23103891, 52.7666193 ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc_y.inverse_transform(regressor.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k0FZPUHVCBsy",
    "outputId": "9e2dcf7e-6c3a-40c2-8afe-b3b5b030ed9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[58.33]\n",
      " [43.72]\n",
      " [14.22]\n",
      " [57.52]\n",
      " [78.15]\n",
      " [95.63]\n",
      " [45.57]\n",
      " [13.89]\n",
      " [34.33]\n",
      " [63.71]\n",
      " [97.23]\n",
      " [81.76]\n",
      " [47.6 ]]\n"
     ]
    }
   ],
   "source": [
    "print(sc_y.inverse_transform((y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a7PeI1OP9Pbo"
   },
   "source": [
    "### Final Accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UGh0RLe1HZnZ",
    "outputId": "ce98cc7a-8f1e-49df-f7a5-c85e2c990a39"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.52654276692948 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "acc = r2_score(sc_y.inverse_transform(y_test),sc_y.inverse_transform(regressor.predict(x_test)))\n",
    "print(acc*100,\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uvWo6vaYyH_2"
   },
   "source": [
    "## Visualizing the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "t335c5431LJF"
   },
   "outputs": [],
   "source": [
    "year = x_visual[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "65gX171DyY5t",
    "outputId": "f97c473c-f8a3-4bb7-9c56-88252637c871"
   },
   "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(year, y_visual)\n",
    "plt.scatter(year, sc_y.inverse_transform(regressor.predict(x_test)))\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"Market Demands\")\n",
    "plt.title(\"Population vs Market Demands\")\n",
    "plt.legend([\"Real values\", \"Predictions\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model in pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "file = open(\"svr_model.pkl\", \"wb\")\n",
    "pickle.dump(regressor, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making a single prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year:-1.6797120493629716\n",
      "population:-1.4694993102709804\n",
      "surplus:-0.7123947573995313\n",
      "[18.48301997]\n"
     ]
    }
   ],
   "source": [
    "year = 1960\n",
    "population = 450547679\n",
    "surplus = -3.9\n",
    "\n",
    "year = (year - 1989.4)/17.503\n",
    "population = (population - 877977753)/290867828.935\n",
    "surplus = (surplus + 0.617)/4.6084\n",
    "new_features = np.array([[ year, population, surplus]])\n",
    "demand = regressor.predict(new_features)\n",
    "demand = (demand * 26.1050) + 52.10958\n",
    "\n",
    "print(\"year:{}\".format(year))\n",
    "print(\"population:{}\".format(population))\n",
    "print(\"surplus:{}\".format(surplus))\n",
    "print(demand)"
   ]
  }
 ],
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