Crop_prediction.ipynb

{
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   "execution_count": 1,
   "metadata": {
    "id": "c-IyvKfv78Nd"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Um7lJqFJTJpT"
   },
   "source": [
    "<H1> Exploratory Analysis</h1>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "e77ElIJw8Ck1",
    "outputId": "4d9e965e-0977-47c2-8de9-191006c001bf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop=pd.read_csv(\"Crop_recommendation.csv\")\n",
    "#type(crop)\n",
    "crop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7M4qlejK8Fvo",
    "outputId": "f523a83b-9217-44a8-dc6f-d68acfcfe197"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ux9V14W28IF3",
    "outputId": "bef38958-4181-424f-ea16-a2eae542821f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2200 entries, 0 to 2199\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   N            2200 non-null   int64  \n",
      " 1   P            2200 non-null   int64  \n",
      " 2   K            2200 non-null   int64  \n",
      " 3   temperature  2200 non-null   float64\n",
      " 4   humidity     2200 non-null   float64\n",
      " 5   ph           2200 non-null   float64\n",
      " 6   rainfall     2200 non-null   float64\n",
      " 7   label        2200 non-null   object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 137.6+ KB\n"
     ]
    }
   ],
   "source": [
    "crop.info() # for all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "VW99_daa8Ona",
    "outputId": "69de99d5-3321-43bf-dc4e-cafaa4651829"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.551818</td>\n",
       "      <td>53.362727</td>\n",
       "      <td>48.149091</td>\n",
       "      <td>25.616244</td>\n",
       "      <td>71.481779</td>\n",
       "      <td>6.469480</td>\n",
       "      <td>103.463655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.917334</td>\n",
       "      <td>32.985883</td>\n",
       "      <td>50.647931</td>\n",
       "      <td>5.063749</td>\n",
       "      <td>22.263812</td>\n",
       "      <td>0.773938</td>\n",
       "      <td>54.958389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.825675</td>\n",
       "      <td>14.258040</td>\n",
       "      <td>3.504752</td>\n",
       "      <td>20.211267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.769375</td>\n",
       "      <td>60.261953</td>\n",
       "      <td>5.971693</td>\n",
       "      <td>64.551686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>25.598693</td>\n",
       "      <td>80.473146</td>\n",
       "      <td>6.425045</td>\n",
       "      <td>94.867624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>84.250000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>28.561654</td>\n",
       "      <td>89.948771</td>\n",
       "      <td>6.923643</td>\n",
       "      <td>124.267508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>43.675493</td>\n",
       "      <td>99.981876</td>\n",
       "      <td>9.935091</td>\n",
       "      <td>298.560117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 N            P            K  temperature     humidity  \\\n",
       "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
       "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
       "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
       "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
       "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
       "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
       "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
       "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
       "\n",
       "                ph     rainfall  \n",
       "count  2200.000000  2200.000000  \n",
       "mean      6.469480   103.463655  \n",
       "std       0.773938    54.958389  \n",
       "min       3.504752    20.211267  \n",
       "25%       5.971693    64.551686  \n",
       "50%       6.425045    94.867624  \n",
       "75%       6.923643   124.267508  \n",
       "max       9.935091   298.560117  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop.describe() #statistics of only numerical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "48L8_RcIU_fk"
   },
   "source": [
    "<h1> Data Visualization</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "k9KR9pKEVEfN",
    "outputId": "e6b6a9dd-012f-41b6-8b40-c0115eafd20c"
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "sns.set_palette(\"husl\")\n",
    "for i in crop.columns[:-1]:\n",
    "    fig,ax=plt.subplots(1,2,figsize=(18,4))\n",
    "    sns.histplot(data=crop,x=i,kde=True,bins=40,ax=ax[0])\n",
    "    sns.boxplot(data=crop,x=i,ax=ax[1])\n",
    "    plt.suptitle(f'Visualizing {i}',size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 511
    },
    "id": "0pFwgIZjVOFj",
    "outputId": "c1a10271-b04a-4713-e003-d9fc6943d12d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking correlation \n",
    "numeric_crop = crop.select_dtypes(include=['float64', 'int64'])\n",
    "figure = plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(numeric_crop.corr(), annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YGV0wubCTm1N"
   },
   "source": [
    "<h1> Data Pre-processing <h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5_NHp0QqTtNL"
   },
   "source": [
    "<h4> 1. Handling Missing Values</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 805
    },
    "id": "xCNOmtmh8QdF",
    "outputId": "ff2457ec-32b1-4d81-cbfa-608efa43c7fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "rice           100\n",
       "maize          100\n",
       "jute           100\n",
       "cotton         100\n",
       "coconut        100\n",
       "papaya         100\n",
       "orange         100\n",
       "apple          100\n",
       "muskmelon      100\n",
       "watermelon     100\n",
       "grapes         100\n",
       "mango          100\n",
       "banana         100\n",
       "pomegranate    100\n",
       "lentil         100\n",
       "blackgram      100\n",
       "mungbean       100\n",
       "mothbeans      100\n",
       "pigeonpeas     100\n",
       "kidneybeans    100\n",
       "chickpea       100\n",
       "coffee         100\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "id": "fXcbT-LX8KRr",
    "outputId": "70d9eca9-d4d2-4d56-bfc4-90e9c9d621a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N              0\n",
       "P              0\n",
       "K              0\n",
       "temperature    0\n",
       "humidity       0\n",
       "ph             0\n",
       "rainfall       0\n",
       "label          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking to see if anything is null\n",
    "crop.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hHPlbUai8Mfa",
    "outputId": "af288fae-4814-4963-ed33-cb32300cdeed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checked for duplicates\n",
    "crop.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Yg9AFQxrT4QI"
   },
   "source": [
    "<h4>2. Label Encoding</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "90sXnTsC8TM5"
   },
   "outputs": [],
   "source": [
    "#encoding\n",
    "crop_dict = {\n",
    "    'rice': 1,\n",
    "    'maize': 2,\n",
    "    'jute': 3,\n",
    "    'cotton': 4,\n",
    "    'coconut': 5,\n",
    "    'papaya': 6,\n",
    "    'orange': 7,\n",
    "    'apple': 8,\n",
    "    'muskmelon': 9,\n",
    "    'watermelon': 10,\n",
    "    'grapes': 11,\n",
    "    'mango': 12,\n",
    "    'banana': 13,\n",
    "    'pomegranate': 14,\n",
    "    'lentil': 15,\n",
    "    'blackgram': 16,\n",
    "    'mungbean': 17,\n",
    "    'mothbeans': 18,\n",
    "    'pigeonpeas': 19,\n",
    "    'kidneybeans': 20,\n",
    "    'chickpea': 21,\n",
    "    'coffee': 22\n",
    "}\n",
    "crop['crop_num']=  crop['label'].map(crop_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
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       "        N   P   K  temperature   humidity        ph    rainfall   label  \\\n",
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       "    N   P   K  temperature   humidity        ph    rainfall  crop_num\n",
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    "crop.drop(['label'],axis=1,inplace=True)\n",
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   "execution_count": 21,
   "metadata": {
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   "source": [
    "X = crop.drop(['crop_num'],axis=1)\n",
    "y = crop['crop_num']"
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       "        N   P   K  temperature   humidity        ph    rainfall\n",
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       "0        1\n",
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       "2        1\n",
       "3        1\n",
       "4        1\n",
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       "2195    22\n",
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   "source": [
    "<h4> 3. Train Test Split</h4>"
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   "execution_count": 24,
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       "        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>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>16.396243</td>\n",
       "      <td>92.181519</td>\n",
       "      <td>6.625539</td>\n",
       "      <td>102.944161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>37</td>\n",
       "      <td>79</td>\n",
       "      <td>19</td>\n",
       "      <td>27.543848</td>\n",
       "      <td>69.347863</td>\n",
       "      <td>7.143943</td>\n",
       "      <td>69.408782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>7</td>\n",
       "      <td>73</td>\n",
       "      <td>25</td>\n",
       "      <td>27.521856</td>\n",
       "      <td>63.132153</td>\n",
       "      <td>7.288057</td>\n",
       "      <td>45.208411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>101</td>\n",
       "      <td>70</td>\n",
       "      <td>48</td>\n",
       "      <td>25.360592</td>\n",
       "      <td>75.031933</td>\n",
       "      <td>6.012697</td>\n",
       "      <td>116.553145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>35.474783</td>\n",
       "      <td>47.972305</td>\n",
       "      <td>6.279134</td>\n",
       "      <td>97.790725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>21.213070</td>\n",
       "      <td>91.353492</td>\n",
       "      <td>7.817846</td>\n",
       "      <td>112.983436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>108</td>\n",
       "      <td>94</td>\n",
       "      <td>47</td>\n",
       "      <td>27.359116</td>\n",
       "      <td>84.546250</td>\n",
       "      <td>6.387431</td>\n",
       "      <td>90.812505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>27.920633</td>\n",
       "      <td>51.779659</td>\n",
       "      <td>6.475449</td>\n",
       "      <td>100.258567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>11</td>\n",
       "      <td>124</td>\n",
       "      <td>204</td>\n",
       "      <td>13.429886</td>\n",
       "      <td>80.066340</td>\n",
       "      <td>6.361141</td>\n",
       "      <td>71.400430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>32</td>\n",
       "      <td>78</td>\n",
       "      <td>22</td>\n",
       "      <td>23.970814</td>\n",
       "      <td>62.355576</td>\n",
       "      <td>7.007038</td>\n",
       "      <td>53.409060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1760 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        N    P    K  temperature   humidity        ph    rainfall\n",
       "1656   17   16   14    16.396243  92.181519  6.625539  102.944161\n",
       "752    37   79   19    27.543848  69.347863  7.143943   69.408782\n",
       "892     7   73   25    27.521856  63.132153  7.288057   45.208411\n",
       "1041  101   70   48    25.360592  75.031933  6.012697  116.553145\n",
       "1179    0   17   30    35.474783  47.972305  6.279134   97.790725\n",
       "...   ...  ...  ...          ...        ...       ...         ...\n",
       "1638   10    5    5    21.213070  91.353492  7.817846  112.983436\n",
       "1095  108   94   47    27.359116  84.546250  6.387431   90.812505\n",
       "1130   11   36   31    27.920633  51.779659  6.475449  100.258567\n",
       "1294   11  124  204    13.429886  80.066340  6.361141   71.400430\n",
       "860    32   78   22    23.970814  62.355576  7.007038   53.409060\n",
       "\n",
       "[1760 rows x 7 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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=42)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MrNOJBzTUSam"
   },
   "source": [
    "<h4> 4. Scaling and Standardization</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TkeFPWVx8mFk",
    "outputId": "7d4493ce-61c6-4dcb-e361-6b16c95275e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12142857, 0.07857143, 0.045     , ..., 0.9089898 , 0.48532225,\n",
       "        0.29685161],\n",
       "       [0.26428571, 0.52857143, 0.07      , ..., 0.64257946, 0.56594073,\n",
       "        0.17630752],\n",
       "       [0.05      , 0.48571429, 0.1       , ..., 0.57005802, 0.58835229,\n",
       "        0.08931844],\n",
       "       ...,\n",
       "       [0.07857143, 0.22142857, 0.13      , ..., 0.43760347, 0.46198144,\n",
       "        0.28719815],\n",
       "       [0.07857143, 0.85      , 0.995     , ..., 0.76763665, 0.44420505,\n",
       "        0.18346657],\n",
       "       [0.22857143, 0.52142857, 0.085     , ..., 0.56099735, 0.54465022,\n",
       "        0.11879596]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "ms = MinMaxScaler()\n",
    "\n",
    "X_train = ms.fit_transform(X_train)\n",
    "X_test = ms.transform(X_test)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V_H1GdByArrS",
    "outputId": "d1d6ab30-4923-45cc-8962-fa52bedb9b22"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-9.03426596e-01, -1.12616170e+00, -6.68506601e-01, ...,\n",
       "         9.36586183e-01,  1.93473784e-01,  5.14970176e-03],\n",
       "       [-3.67051340e-01,  7.70358846e-01, -5.70589522e-01, ...,\n",
       "        -1.00470485e-01,  8.63917548e-01, -6.05290566e-01],\n",
       "       [-1.17161422e+00,  5.89737842e-01, -4.53089028e-01, ...,\n",
       "        -3.82774991e-01,  1.05029771e+00, -1.04580687e+00],\n",
       "       ...,\n",
       "       [-1.06433917e+00, -5.24091685e-01, -3.35588533e-01, ...,\n",
       "        -8.98381379e-01, -6.34357580e-04, -4.37358211e-02],\n",
       "       [-1.06433917e+00,  2.12501638e+00,  3.05234239e+00, ...,\n",
       "         3.86340190e-01, -1.48467347e-01, -5.69036842e-01],\n",
       "       [-5.01145154e-01,  7.40255346e-01, -5.11839275e-01, ...,\n",
       "        -4.18045489e-01,  6.86860180e-01, -8.96531475e-01]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "X_train\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kFgtPLppUvJ8"
   },
   "source": [
    "<h1> Applying Different Models </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression with accuracy: 0.9295454545454546\n",
      "Precision: 0.9414068512561335\n",
      "Recall: 0.9295454545454546\n",
      "F1-Score: 0.9275323318484936\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "# Initialize and train model\n",
    "model = LogisticRegression(solver='liblinear', max_iter=1000)\n",
    "model.fit(X_train, y_train)\n",
    "ypred = model.predict(X_test)\n",
    "\n",
    "# Calculate and print metrics\n",
    "accuracy = accuracy_score(y_test, ypred)\n",
    "precision = precision_score(y_test, ypred, average='weighted')\n",
    "recall = recall_score(y_test, ypred, average='weighted')\n",
    "f1 = f1_score(y_test, ypred, average='weighted')\n",
    "\n",
    "print(f\"Logistic Regression with accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "print(f\"Recall: {recall}\")\n",
    "print(f\"F1-Score: {f1}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine with accuracy: 0.9681818181818181\n",
      "Precision: 0.9715171192443919\n",
      "Recall: 0.9681818181818181\n",
      "F1-Score: 0.9680271341017991\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Initialize and train model\n",
    "model = SVC()\n",
    "model.fit(X_train, y_train)\n",
    "ypred = model.predict(X_test)\n",
    "\n",
    "# Calculate and print metrics\n",
    "accuracy = accuracy_score(y_test, ypred)\n",
    "precision = precision_score(y_test, ypred, average='weighted')\n",
    "recall = recall_score(y_test, ypred, average='weighted')\n",
    "f1 = f1_score(y_test, ypred, average='weighted')\n",
    "\n",
    "print(f\"Support Vector Machine with accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "print(f\"Recall: {recall}\")\n",
    "print(f\"F1-Score: {f1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors with accuracy: 0.9590909090909091\n",
      "Precision: 0.9653897981006235\n",
      "Recall: 0.9590909090909091\n",
      "F1-Score: 0.9590011070163019\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Initialize and train model\n",
    "model = KNeighborsClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "ypred = model.predict(X_test)\n",
    "\n",
    "# Calculate and print metrics\n",
    "accuracy = accuracy_score(y_test, ypred)\n",
    "precision = precision_score(y_test, ypred, average='weighted')\n",
    "recall = recall_score(y_test, ypred, average='weighted')\n",
    "f1 = f1_score(y_test, ypred, average='weighted')\n",
    "\n",
    "print(f\"K-Nearest Neighbors with accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "print(f\"Recall: {recall}\")\n",
    "print(f\"F1-Score: {f1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes with variance smoothing and accuracy: 0.9840909090909091\n",
      "Recall: 0.9840909090909091\n",
      "F1-Score: 0.9845069852234487\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB(var_smoothing=1e-2)\n",
    "model.fit(X_train_scaled, y_train)\n",
    "ypred = model.predict(X_test_scaled)\n",
    "\n",
    "accuracy = accuracy_score(y_test, ypred)\n",
    "precision = precision_score(y_test, ypred, average='weighted')\n",
    "recall = recall_score(y_test, ypred, average='weighted')\n",
    "f1 = f1_score(y_test, ypred, average='weighted')\n",
    "\n",
    "print(f\"Gaussian Naive Bayes with variance smoothing and accuracy: {accuracy}\")\n",
    "print(f\"Recall: {recall}\")\n",
    "print(f\"F1-Score: {f1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with accuracy: 0.9863636363636363\n",
      "Precision: 0.9868055555555556\n",
      "Recall: 0.9863636363636363\n",
      "F1-Score: 0.9863152866630287\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "#checking precision,f1score and recall\n",
    "\n",
    "# Initialize and train model\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "ypred = model.predict(X_test)\n",
    "\n",
    "# Calculate and print metrics\n",
    "accuracy = accuracy_score(y_test, ypred)\n",
    "precision = precision_score(y_test, ypred, average='weighted')\n",
    "recall = recall_score(y_test, ypred, average='weighted')\n",
    "f1 = f1_score(y_test, ypred, average='weighted')\n",
    "\n",
    "print(f\"Decision Tree with accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "print(f\"Recall: {recall}\")\n",
    "print(f\"F1-Score: {f1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest with accuracy: 0.9931818181818182\n",
      "Precision: 0.9937348484848485\n",
      "Recall: 0.9931818181818182\n",
      "F1-Score: 0.9931754816901672\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Initialize and train model\n",
    "#checking precision,f1score and recall\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "ypred = model.predict(X_test)\n",
    "\n",
    "# Calculate and print metrics\n",
    "accuracy = accuracy_score(y_test, ypred)\n",
    "precision = precision_score(y_test, ypred, average='weighted')\n",
    "recall = recall_score(y_test, ypred, average='weighted')\n",
    "f1 = f1_score(y_test, ypred, average='weighted')\n",
    "\n",
    "print(f\"Random Forest with accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "print(f\"Recall: {recall}\")\n",
    "print(f\"F1-Score: {f1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bagging Classifier with accuracy: 0.990909090909091\n",
      "Precision: 0.9911616161616161\n",
      "Recall: 0.990909090909091\n",
      "F1-Score: 0.9909147163309975\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "# Initialize and train model\n",
    "model = BaggingClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "ypred = model.predict(X_test)\n",
    "\n",
    "# Calculate and print metrics\n",
    "accuracy = accuracy_score(y_test, ypred)\n",
    "precision = precision_score(y_test, ypred, average='weighted')\n",
    "recall = recall_score(y_test, ypred, average='weighted')\n",
    "f1 = f1_score(y_test, ypred, average='weighted')\n",
    "\n",
    "print(f\"Bagging Classifier with accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "print(f\"Recall: {recall}\")\n",
    "print(f\"F1-Score: {f1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Final Code with Random Forest Classifier</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6vV9ef8H8yHl",
    "outputId": "a7095d43-4a4f-43c3-a786-6f3537bd5f12"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted crop for the first set of input features is: Coffee\n",
      "The predicted crop for the second set of input features is: Mothbeans\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "crop_dict = {\n",
    "    1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
    "    8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
    "    14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
    "    19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"\n",
    "}\n",
    "\n",
    "def suggest_crop(features):\n",
    "    features = np.array(features).reshape(1, -1)\n",
    "    predicted_crop_num = rf.predict(features)[0]\n",
    "    predicted_crop_name = crop_dict.get(predicted_crop_num, \"Unknown Crop\")\n",
    "    return predicted_crop_name\n",
    "\n",
    "# Example valid input (corresponding to [N, P, K, temperature, humidity, ph, rainfall])\n",
    "example_input_1 = [100, 30, 30, 25, 60, 6, 170]\n",
    "example_input_2 = [30,50,40,30.50,50.0,30,40]\n",
    "\n",
    "predicted_crop_name_1 = suggest_crop(example_input_1)\n",
    "predicted_crop_name_2 = suggest_crop(example_input_2)\n",
    "print(f\"The predicted crop for the first set of input features is: {predicted_crop_name_1}\")\n",
    "print(f\"The predicted crop for the second set of input features is: {predicted_crop_name_2}\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "base",
   "language": "python",
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