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pred.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import ColumnTransformer
+import tkinter as tk
+from tkinter import ttk
+
+# Load your dataset (replace 'your_dataset.csv' with the actual file name)
+data = pd.read_csv('C:/Users/Windows/Downloads/pred_data.csv', encoding='ISO-8859-1')
+
+# Data Preprocessing
+data['arrival_date'] = pd.to_datetime(data['arrival_date'])
+data['day'] = data['arrival_date'].dt.day
+data['month'] = data['arrival_date'].dt.month
+data['year'] = data['arrival_date'].dt.year
+data.drop(['arrival_date'], axis=1, inplace=True)
+
+# Define features and target variable
+X = data.drop(['min_price', 'max_price', 'modal_price'], axis=1)
+y = data[['min_price', 'max_price', 'modal_price']]
+
+# Use OneHotEncoder to encode categorical variables
+encoder = ColumnTransformer(
+    transformers=[
+        ('cat', OneHotEncoder(), ['state', 'district', 'market', 'commodity', 'variety'])
+    ],
+    remainder='passthrough'
+)
+
+X_encoded = encoder.fit_transform(X)
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)
+
+# Train the Linear Regression model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# GUI with Tkinter
+def predict_prices():
+    input_data = {
+        'state': state_var.get(),
+        'district': district_var.get(),
+        'market': market_var.get(),
+        'commodity': commodity_var.get(),
+        'variety': variety_var.get(),
+        'arrival_date': pd.to_datetime(arrival_date_var.get())
+    }
+
+    input_df = pd.DataFrame([input_data])
+    input_df['day'] = input_df['arrival_date'].dt.day
+    input_df['month'] = input_df['arrival_date'].dt.month
+    input_df['year'] = input_df['arrival_date'].dt.year
+    input_df.drop(['arrival_date'], axis=1, inplace=True)
+
+    input_encoded = encoder.transform(input_df)
+
+    # Make predictions
+    predictions = model.predict(input_encoded)[0]
+
+    # Update the output labels
+    min_price_label.config(text=f"Min Price: {predictions[0]}")
+    max_price_label.config(text=f"Max Price: {predictions[1]}")
+    modal_price_label.config(text=f"Modal Price: {predictions[2]}")
+
+# Create the Tkinter window
+window = tk.Tk()
+window.title("Price Prediction App")
+
+# Create and place labels and entry widgets
+state_label = ttk.Label(window, text="State:")
+state_label.grid(row=0, column=0, padx=10, pady=10)
+state_var = tk.StringVar()
+state_entry = ttk.Entry(window, textvariable=state_var)
+state_entry.grid(row=0, column=1, padx=10, pady=10)
+
+# Similar setup for other input fields (district, market, commodity, variety, arrival_date)
+# Create and place labels and entry widgets for other input fields
+district_label = ttk.Label(window, text="District:")
+district_label.grid(row=1, column=0, padx=10, pady=10)
+district_var = tk.StringVar()
+district_entry = ttk.Entry(window, textvariable=district_var)
+district_entry.grid(row=1, column=1, padx=10, pady=10)
+
+market_label = ttk.Label(window, text="Market:")
+market_label.grid(row=2, column=0, padx=10, pady=10)
+market_var = tk.StringVar()
+market_entry = ttk.Entry(window, textvariable=market_var)
+market_entry.grid(row=2, column=1, padx=10, pady=10)
+
+commodity_label = ttk.Label(window, text="Commodity:")
+commodity_label.grid(row=3, column=0, padx=10, pady=10)
+commodity_var = tk.StringVar()
+commodity_entry = ttk.Entry(window, textvariable=commodity_var)
+commodity_entry.grid(row=3, column=1, padx=10, pady=10)
+
+variety_label = ttk.Label(window, text="Variety:")
+variety_label.grid(row=4, column=0, padx=10, pady=10)
+variety_var = tk.StringVar()
+variety_entry = ttk.Entry(window, textvariable=variety_var)
+variety_entry.grid(row=4, column=1, padx=10, pady=10)
+
+arrival_date_label = ttk.Label(window, text="Arrival Date:")
+arrival_date_label.grid(row=5, column=0, padx=10, pady=10)
+arrival_date_var = tk.StringVar()
+arrival_date_entry = ttk.Entry(window, textvariable=arrival_date_var)
+arrival_date_entry.grid(row=5, column=1, padx=10, pady=10)
+
+# Create and place a button to trigger the prediction
+predict_button = ttk.Button(window, text="Predict Prices", command=predict_prices)
+predict_button.grid(row=6, column=0, columnspan=2, pady=10)
+
+# Create and place labels to display the output
+min_price_label = ttk.Label(window, text="Min Price: ")
+min_price_label.grid(row=7, column=0, columnspan=2)
+
+max_price_label = ttk.Label(window, text="Max Price: ")
+max_price_label.grid(row=8, column=0, columnspan=2)
+
+modal_price_label = ttk.Label(window, text="Modal Price: ")
+modal_price_label.grid(row=9, column=0, columnspan=2)
+
+# Run the Tkinter event loop
+window.mainloop()