smart-phone-analysis

Prediction Models/smartphone_rate_prediction/README.md

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+# Smartphone Price Prediction Using Random Forest Regressor
+
+## 🎯 Goal
+The goal of this project is to build a predictive model that estimates smartphone prices based on their ratings and the number of reviews. The project uses a **Random Forest Regressor** model to predict prices from these features, aiming to deliver accurate pricing insights.
+
+## 🧵 Dataset
+The dataset contains various attributes of smartphones, including:
+- **Price** (Target variable)
+- **Rating** (Independent variable)
+- **Reviews** (Independent variable)
+
+The dataset is loaded from a CSV file, which needs to be provided in the file path specified during execution.
+
+## 🧾 Description
+This project involves the following steps:
+1. **Data Preprocessing**:
+   - Handling missing values in the price column.
+   - Cleaning the rating column by replacing non-numeric values and imputing missing values with the mean.
+   - Converting the price and reviews columns into numeric format.
+   - Dropping any remaining rows with missing data.
+
+2. **Model Building**:
+   - Features: The model uses smartphone **Rating** and **Reviews** to predict **Price**.
+   - A **Random Forest Regressor** model is built to predict the price using 80% of the data for training and 20% for testing.
+
+3. **Model Evaluation**:
+   - **RMSE (Root Mean Squared Error)** and **R² Score** are calculated to evaluate the model's performance.
+
+4. **Data Visualization**:
+   - Visualization of the price distribution.
+   - Feature importance plot to show how each feature contributes to the model.
+   - Actual vs. predicted prices scatter plot.
+
+## 📊 Features and Workflow
+
+1. **Data Preprocessing**:
+   - Remove rows with missing prices.
+   - Convert ratings and reviews into numeric form.
+   - Remove commas from the price column and convert it to a numeric format.
+   - Drop any remaining rows with missing values.
+
+2. **Random Forest Regressor**:
+   - Built using the `sklearn` library.
+   - `n_estimators=100` for building 100 trees in the forest.
+   - Data split into training (80%) and testing (20%) sets.
+
+3. **Model Performance**:
+   - **RMSE**: Measures the average difference between the actual and predicted prices.
+   - **R² Score**: Indicates how well the independent variables explain the variance in the price.
+
+4. **Visualizations**:
+   - **Price Distribution**: Histogram and KDE plot showing the distribution of smartphone prices.
+   - **Feature Importance**: Bar plot indicating which features contribute the most to price prediction.
+   - **Actual vs. Predicted Prices**: Scatter plot comparing the predicted and actual prices for the test set.
+
+## 🧮 Performance Metrics
+- **RMSE (Root Mean Squared Error)**: A measure of the model’s error in predicting the price.
+- **R² Score**: A metric that shows the proportion of variance in the dependent variable explained by the independent variables.
+
+## 📚 Libraries Needed
+- `pandas`: For data manipulation.
+- `numpy`: For numerical operations.
+- `matplotlib` & `seaborn`: For data visualization.
+- `sklearn`: For model building and evaluation.
+
+## 🔧 How to Run
+1. Install the required libraries:
+   ```bash
+   pip install pandas numpy matplotlib seaborn scikit-learn
+   ```
+2. Load the dataset and run the script:
+   ```python
+   df = pd.read_csv('path_to_your_dataset.csv')  # Update with the actual path
+   ```
+3. The script will preprocess the data, train the Random Forest model, and provide predictions along with visualizations.
+
+## 📈 Results
+- **RMSE** and **R² Score** will be printed in the console after running the script.
+- Visualizations (Price distribution, feature importance, actual vs predicted prices) will be shown to help analyze the results.
+
+## 📢 Conclusion
+This project demonstrates how **Random Forest Regressor** can be used to predict smartphone prices based on features like ratings and reviews. The visualizations provide a deeper understanding of the model's performance and feature contributions.
+
+## ✒️ Author
+**Benak Deepak**
+
+- LinkedIn: [www.linkedin.com/in/benak-deepak-210918254](www.linkedin.com/in/benak-deepak-210918254)
+```