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Sowing Success: How Machine Learning Helps Farmers Select the Best Crops

Measuring essential soil metrics such as nitrogen, phosphorous, potassium levels, and pH value is an important aspect of assessing soil condition. However, it can be an expensive and time-consuming process, which can cause farmers to prioritize which metrics to measure based on their budget constraints.

Farmers have various options when it comes to deciding which crop to plant each season. Their primary objective is to maximize the yield of their crops, taking into account different factors. One crucial factor that affects crop growth is the condition of the soil in the field, which can be assessed by measuring basic elements such as nitrogen and potassium levels. Each crop has an ideal soil condition that ensures optimal growth and maximum yield.

A farmer reached out to you as a machine learning expert for assistance in selecting the best crop for his field. They've provided you with a dataset called soil_measures.csv, which contains:

  • "N": Nitrogen content ratio in the soil
  • "P": Phosphorous content ratio in the soil
  • "K": Potassium content ratio in the soil
  • "pH" value of the soil
  • "crop": categorical values that contain various crops (target variable).

Each row in this dataset represents various measures of the soil in a particular field. Based on these measurements, the crop specified in the "crop" column is the optimal choice for that field.

In this project, you will build multi-class classification models to predict the type of "crop" and identify the single most importance feature for predictive performance.

# All required libraries are imported here for you.
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics

# Load the dataset
crops = pd.read_csv("soil_measures.csv")

# Check for missing values
missing_values = crops.isnull().sum()
print("Missing values:")
print(missing_values)

# Check for unique crop types
crop_types = crops['crop'].unique()
print("\nCrop types:")
print(crop_types)
# Features (X) and target variable (y)
X = crops.drop(columns=['crop'])
y = crops['crop']

# Splitting the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
from sklearn.metrics import accuracy_score

# Dictionary to store each feature's predictive performance
feature_performance = {}

# Loop through the features
for feature in X.columns:
    # Selecting only one feature
    X_feature = X[[feature]]
    
    # Splitting the data into training and test sets
    X_train_feature, X_test_feature, y_train, y_test =     train_test_split(X_feature, y, test_size=0.3, random_state=42)
    
    # Creating and fitting the logistic regression model
    model = LogisticRegression(max_iter=1000)
    model.fit(X_train_feature, y_train)
    
    # Making predictions
    y_pred = model.predict(X_test_feature)
    
    # Evaluating the performance of the feature
    accuracy = accuracy_score(y_test, y_pred)
    feature_performance[feature] = accuracy

# Displaying the predictive performance of each feature
print("Predictive performance of each feature:")
for feature, performance in feature_performance.items():
    print(f"{feature}: {performance}")
# Finding the feature with the highest predictive performance
best_feature = max(feature_performance, key=feature_performance.get)
best_performance = feature_performance[best_feature]

# Creating the best_predictive_feature variable
best_predictive_feature = {best_feature: best_performance}

# Displaying the best predictive feature
print("Best predictive feature:")
print(best_predictive_feature)