Create app.py

app.py

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+# Import necessary libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import r2_score, mean_absolute_error
+from transformers import pipeline
+import streamlit as st
+
+# Step 1: Data Collection
+def load_data(file_path):
+    data = pd.read_csv(file_path)
+    return data
+
+# Step 2: Data Cleaning
+def clean_data(data):
+    data.dropna(inplace=True)
+    return data
+
+# Step 3: Exploratory Data Analysis (EDA)
+def perform_eda(data):
+    st.write(data.describe())
+    st.write(data.info())
+
+# Step 4: Data Visualization
+def visualize_data(data):
+    plt.figure(figsize=(10, 6))
+    sns.histplot(data['price'], kde=True)
+    plt.title('Price Distribution')
+    plt.show()
+
+    plt.figure(figsize=(10, 6))
+    sns.boxplot(x='brand', y='price', data=data)
+    plt.title('Price by Brand')
+    plt.show()
+
+# Step 5: Feature Engineering
+def encode_features(data):
+    le = LabelEncoder()
+    categorical_columns = ['brand', 'processor', 'Ram_type', 'ROM_type', 'GPU', 'OS']
+    for col in categorical_columns:
+        data[col] = le.fit_transform(data[col])
+    return data
+
+# Step 6: Machine Learning Modeling
+def build_model(data):
+    X = data.drop(['price'], axis=1)
+    y = data['price']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    
+    # Model Evaluation
+    st.write(f'R² Score: {r2_score(y_test, y_pred)}')
+    st.write(f'Mean Absolute Error: {mean_absolute_error(y_test, y_pred)}')
+    
+    return model
+
+# Step 7: NLP Analysis using Hugging Face (if any text data)
+def analyze_text(feedback_data):
+    sentiment_analysis = pipeline('sentiment-analysis')
+    feedback_data['sentiment'] = feedback_data['feedback'].apply(lambda x: sentiment_analysis(x)[0]['label'])
+    return feedback_data
+
+# Step 8: User Interaction with Streamlit
+def main():
+    st.title("Laptop Price Predictor")
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    
+    if uploaded_file is not None:
+        data = load_data(uploaded_file)
+        data = clean_data(data)
+        
+        st.subheader("Exploratory Data Analysis")
+        perform_eda(data)
+        
+        st.subheader("Data Visualization")
+        visualize_data(data)
+        
+        st.subheader("Feature Engineering")
+        data = encode_features(data)
+        
+        st.subheader("Machine Learning Model")
+        model = build_model(data)
+        
+        st.subheader("Make Predictions")
+        if st.button('Predict'):
+            predictions = model.predict(data.drop(['price'], axis=1))
+            st.write(predictions)
+            plt.figure(figsize=(10, 6))
+            sns.histplot(predictions, kde=True)
+            plt.title('Predicted Price Distribution')
+            plt.show()
+
+        # NLP Analysis (if applicable)
+        # st.subheader("NLP Analysis")
+        # feedback_data = load_feedback_data()  # Assuming a function to load text data
+        # feedback_data = analyze_text(feedback_data)
+        # st.write(feedback_data)
+
+if __name__ == "__main__":
+    main()