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It aims to help farmers make data-driven decisions, boosting productivity and sustainability. + +## **Features** +- **Multiple ML Models**: Utilizes Logistic Regression, Naive Bayes, SVM, Random Forest, and more. +- **Flask-based Web App**: User-friendly interface for real-time crop recommendations. +- **Scalable Solution**: Adaptable for different regions and climates. + +## **Tech Stack** +- **Python**, **Scikit-Learn** for machine learning +- **Flask** for web application +- **Pickle** for model persistence +- **Jupyter Notebook** for development + +## **How to Run the Application** +1. **Clone the repository**: + ```bash + git clone && cd crop-recommendation-system + ``` + +2. **Install dependencies**: + ```bash + pip install -r requirements.txt + ``` + +3. **Train the Model (Optional)**: + Use `crop_main.ipynb` to train the model or load the existing one. + ```python + import pickle + model = pickle.load(open('model.pkl', 'rb')) + ``` + +4. **Run the Flask App**: + ```bash + python app.py + ``` + + +5. **Sample Input Example**: + ``` + N = 10, P = 10, K = 10 + Temperature = 15.0°C + Humidity = 80% + pH = 4.5 + Rainfall = 10 mm + ``` + +## **Troubleshooting** +- **Pickle Load Error**: Ensure the **same scikit-learn version** used for training is installed. +- **Alternative with Joblib**: + ```python + import joblib + model = joblib.load('model.pkl') + ``` + diff --git a/Recommendation Systems/Crop_recommendation System/crop_main.ipynb b/Recommendation Systems/Crop_recommendation System/crop_main.ipynb new file mode 100644 index 00000000..34dad54a --- /dev/null +++ b/Recommendation Systems/Crop_recommendation System/crop_main.ipynb @@ -0,0 +1,1907 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6bdfd636", + "metadata": {}, + "source": [ + "# Import Libaries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7bee9b73", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Noor Saeed\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.1' currently installed).\n", + " from pandas.core.computation.check import NUMEXPR_INSTALLED\n", + "C:\\Users\\Noor Saeed\\anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.4' currently installed).\n", + " from pandas.core import (\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "2822305c", + "metadata": {}, + "source": [ + "# Importing Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5b6f8884", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "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": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crop.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "1056bfba", + "metadata": {}, + "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": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crop['label'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "3e3af150", + "metadata": {}, + "source": [ + "# Encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "8c35d395", + "metadata": {}, + "outputs": [], + "source": [ + "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": 25, + "id": "b1a53f7f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NPKtemperaturehumidityphrainfalllabelcrop_num
090424320.87974482.0027446.502985202.935536rice1
185584121.77046280.3196447.038096226.655537rice1
260554423.00445982.3207637.840207263.964248rice1
374354026.49109680.1583636.980401242.864034rice1
478424220.13017581.6048737.628473262.717340rice1
..............................
2195107343226.77463766.4132696.780064177.774507coffee22
219699152727.41711256.6363626.086922127.924610coffee22
2197118333024.13179767.2251236.362608173.322839coffee22
2198117323426.27241852.1273946.758793127.175293coffee22
2199104183023.60301660.3964756.779833140.937041coffee22
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2200 rows × 9 columns

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NPKtemperaturehumidityphrainfallcrop_num
090424320.87974482.0027446.502985202.9355361
185584121.77046280.3196447.038096226.6555371
260554423.00445982.3207637.840207263.9642481
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478424220.13017581.6048737.628473262.7173401
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" + ], + "text/plain": [ + " N P K temperature humidity ph rainfall crop_num\n", + "0 90 42 43 20.879744 82.002744 6.502985 202.935536 1\n", + "1 85 58 41 21.770462 80.319644 7.038096 226.655537 1\n", + "2 60 55 44 23.004459 82.320763 7.840207 263.964248 1\n", + "3 74 35 40 26.491096 80.158363 6.980401 242.864034 1\n", + "4 78 42 42 20.130175 81.604873 7.628473 262.717340 1" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crop.drop(['label'],axis=1,inplace=True)\n", + "crop.head()" + ] + }, + { + "cell_type": "markdown", + "id": "a5494675", + "metadata": {}, + "source": [ + "# Train Test Split" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "5a049f55", + "metadata": {}, + "outputs": [], + "source": [ + "X = crop.drop(['crop_num'],axis=1)\n", + "y = crop['crop_num']" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9d223a69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NPKtemperaturehumidityphrainfall
090424320.87974482.0027446.502985202.935536
185584121.77046280.3196447.038096226.655537
260554423.00445982.3207637.840207263.964248
374354026.49109680.1583636.980401242.864034
478424220.13017581.6048737.628473262.717340
........................
2195107343226.77463766.4132696.780064177.774507
219699152727.41711256.6363626.086922127.924610
2197118333024.13179767.2251236.362608173.322839
2198117323426.27241852.1273946.758793127.175293
2199104183023.60301660.3964756.779833140.937041
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2200 rows × 7 columns

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NPKtemperaturehumidityphrainfall
165617161416.39624392.1815196.625539102.944161
75237791927.54384869.3478637.14394369.408782
8927732527.52185663.1321537.28805745.208411
1041101704825.36059275.0319336.012697116.553145
11790173035.47478347.9723056.27913497.790725
........................
1638105521.21307091.3534927.817846112.983436
1095108944727.35911684.5462506.38743190.812505
113011363127.92063351.7796596.475449100.258567
12941112420413.42988680.0663406.36114171.400430
86032782223.97081462.3555767.00703853.409060
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1760 rows × 7 columns

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" + ], + "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": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train" + ] + }, + { + "cell_type": "markdown", + "id": "ab13cdf8", + "metadata": {}, + "source": [ + "\n", + "# Scale the features using MinMaxScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "f19981a7", + "metadata": {}, + "outputs": [], + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "f3f50c64", + "metadata": {}, + "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": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train" + ] + }, + { + "cell_type": "markdown", + "id": "752a08ae", + "metadata": {}, + "source": [ + "# Training Models" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "ac6ef55e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logistic Regression with accuracy : 0.9181818181818182\n", + "Confusion matrix : [[16 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 20 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 6 0 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]\n", + " [ 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 3 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 17 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 10 0 0 1 0 0 13 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 18 2 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17]]\n", + "==========================================================\n", + "Naive Bayes with accuracy : 0.9954545454545455\n", + "Confusion matrix : [[17 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17]]\n", + "==========================================================\n", + "Support Vector Machine with accuracy : 0.9681818181818181\n", + "Confusion matrix : [[14 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 20 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]\n", + " [ 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 19 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 21 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 20 2 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17]]\n", + "==========================================================\n", + "K-Nearest Neighbors with accuracy : 0.9704545454545455\n", + "Confusion matrix : [[14 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 1 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 19 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 21 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 20 2 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17]]\n", + "==========================================================\n", + "Decision Tree with accuracy : 0.9818181818181818\n", + "Confusion matrix : [[17 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 20 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 3 0 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 22 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17]]\n", + "==========================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random Forest with accuracy : 0.9931818181818182\n", + "Confusion matrix : [[17 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 23 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17]]\n", + "==========================================================\n", + "Bagging with accuracy : 0.9886363636363636\n", + "Confusion matrix : [[17 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 1 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 22 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17]]\n", + "==========================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Noor Saeed\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AdaBoost with accuracy : 0.09545454545454546\n", + "Confusion matrix : [[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0]]\n", + "==========================================================\n", + "Gradient Boosting with accuracy : 0.9818181818181818\n", + "Confusion matrix : [[15 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 20 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 1 0 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 23 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 22 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17]]\n", + "==========================================================\n", + "Extra Trees with accuracy : 0.8863636363636364\n", + "Confusion matrix : [[12 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 18 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]\n", + " [ 6 0 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2]\n", + " [ 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 2 0 0 0 0 20 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 16 1 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 1 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 15 0 3 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 17 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 2 2 17 1 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 16 4 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 4 15 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 16]]\n", + "==========================================================\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.tree import ExtraTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.ensemble import BaggingClassifier\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.ensemble import AdaBoostClassifier\n", + "from sklearn.metrics import accuracy_score,confusion_matrix\n", + "\n", + "# create instances of all models\n", + "models = {\n", + " 'Logistic Regression': LogisticRegression(),\n", + " 'Naive Bayes': GaussianNB(),\n", + " 'Support Vector Machine': SVC(),\n", + " 'K-Nearest Neighbors': KNeighborsClassifier(),\n", + " 'Decision Tree': DecisionTreeClassifier(),\n", + " 'Random Forest': RandomForestClassifier(),\n", + " 'Bagging': BaggingClassifier(),\n", + " 'AdaBoost': AdaBoostClassifier(),\n", + " 'Gradient Boosting': GradientBoostingClassifier(),\n", + " 'Extra Trees': ExtraTreeClassifier(),\n", + "}\n", + "\n", + "\n", + "for name, model in models.items():\n", + " model.fit(X_train,y_train)\n", + " ypred = model.predict(X_test)\n", + " \n", + " print(f\"{name} with accuracy : {accuracy_score(y_test,ypred)}\")\n", + " print(\"Confusion matrix : \",confusion_matrix(y_test,ypred))\n", + " print(\"==========================================================\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "4659be4d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9931818181818182" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# finally selected randomforest model\n", + "\n", + "rfc = RandomForestClassifier()\n", + "rfc.fit(X_train,y_train)\n", + "ypred = rfc.predict(X_test)\n", + "accuracy_score(y_test,ypred)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "3e72d7f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9954545454545455" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# or gausianjb\n", + "gnb = GaussianNB()\n", + "gnb.fit(X_train,y_train)\n", + "ypred = gnb.predict(X_test)\n", + "accuracy_score(y_test,ypred)" + ] + }, + { + "cell_type": "markdown", + "id": "859d9922", + "metadata": {}, + "source": [ + "# Predictive System" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "17f3a3fe", + "metadata": {}, + "outputs": [], + "source": [ + "def recommendation(N,P,k,temperature,humidity,ph,rainfal):\n", + " features = np.array([[N,P,k,temperature,humidity,ph,rainfal]])\n", + " transformed_features = ms.fit_transform(features)\n", + " prediction = rfc.predict(transformed_features)\n", + " print(prediction)\n", + " return prediction[0] " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "64ffd9d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9]\n", + "Muskmelon is a best crop to be cultivated \n" + ] + } + ], + "source": [ + "# new inputs\n", + "\n", + "\n", + "N = 40\n", + "P = 50\n", + "k = 50\n", + "temperature = 40.0\n", + "humidity = 20\n", + "ph = 100\n", + "rainfall = 100\n", + "\n", + "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n", + "\n", + "crop_dict = {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", + "if predict in crop_dict:\n", + " crop = crop_dict[predict]\n", + " print(\"{} is a best crop to be cultivated \".format(crop))\n", + "else:\n", + " print(\"Sorry are not able to recommend a proper crop for this environment\")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "2ea8ffda", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9]\n", + "Muskmelon is a best crop to be cultivated \n" + ] + } + ], + "source": [ + "# new inputs 2\n", + "\n", + "\n", + "N = 100\n", + "P = 90\n", + "k = 100\n", + "temperature = 50.0\n", + "humidity = 90.0\n", + "ph = 100\n", + "rainfall = 202.0\n", + "\n", + "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n", + "\n", + "crop_dict = {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", + "if predict in crop_dict:\n", + " crop = crop_dict[predict]\n", + " print(\"{} is a best crop to be cultivated \".format(crop))\n", + "else:\n", + " print(\"Sorry are not able to recommend a proper crop for this environment\")" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "d0dccd4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9]\n", + "Muskmelon is a best crop to be cultivated \n" + ] + } + ], + "source": [ + "# new inputs 2\n", + "N = 10\n", + "P = 10\n", + "k = 10\n", + "temperature = 15.0\n", + "humidity = 80.0\n", + "ph = 4.5\n", + "rainfall = 10.0\n", + "\n", + "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n", + "\n", + "crop_dict = {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", + "if predict in crop_dict:\n", + " crop = crop_dict[predict]\n", + " print(\"{} is a best crop to be cultivated \".format(crop))\n", + "else:\n", + " print(\"Sorry are not able to recommend a proper crop for this environment\")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "fa3d3b8c", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "pickle.dump(rfc,open('model.pkl','wb'))\n", + "pickle.dump(ms,open('minmaxscaler.pkl','wb'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a55a48a3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c97733fc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Recommendation Systems/Crop_recommendation System/img.jpg b/Recommendation Systems/Crop_recommendation System/img.jpg new file mode 100644 index 00000000..23d7a32f Binary files /dev/null and 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