search

UppuluriKalyani / ML-Nexus

ML Nexus is an open-source collection of machine learning projects, covering topics like neural networks, computer vision, and NLP. Whether you're a beginner or expert, contribute, collaborate, and grow together in the world of AI. Join us to shape the future of machine learning!
https://discord.gg/tctW47VN
MIT License
59 stars 108 forks source link

Feature request Smart-Phone prediction #688

Open BenakDeepak opened 3 hours ago

BenakDeepak commented 3 hours ago

Problem Description

The current script is designed to predict the 'Price' of a smartphone based on its 'Rating' and 'Reviews'. However, it may lack additional predictive features, and the dataset may require further analysis to identify potential improvements.

Solution Ideas

  1. Data Cleaning Enhancements:

    • Explore additional columns in the dataset that could serve as predictors (e.g., brand, model, storage capacity) for better accuracy.
    • Analyze the distribution of the 'Rating' column after filling NaN values to ensure it reflects a realistic distribution.

  2. Model Optimization:

    • Hyperparameter tuning for the RandomForestRegressor (e.g., n_estimators, max_depth) could yield better performance. Try using GridSearchCV for optimization.
    • Explore other models like GradientBoostingRegressor or XGBoost and compare their performance with the current model.

  3. Evaluation Metrics:

    • Consider adding Mean Absolute Error (MAE) alongside RMSE for a more comprehensive error evaluation.

  4. Feature Engineering:

    • Create derived features if possible, such as logarithmic transformation on the 'Reviews' column if it has a high range or adding interaction terms between 'Rating' and 'Reviews'.

Approach to Follow

  1. Data Exploration:

    • Conduct exploratory data analysis (EDA) on the dataset to identify patterns, distributions, and relationships between 'Price' and other features.
    • Use correlation heatmaps to identify the most significant predictors of 'Price'.

  2. Model Tuning:

    • Use GridSearchCV or RandomizedSearchCV to find the best parameters for RandomForestRegressor.
    • Test additional algorithms for comparison.

  3. Results Visualization:

    • Enhance the actual vs. predicted prices plot by including confidence intervals or histograms of the residuals.
    • Display the model’s performance summary with key metrics in a well-organized output section.

Additional Context

Incorporating a robust EDA section before training will help ensure the model can capture more patterns from the data. Also, displaying training and validation error would help in evaluating overfitting or underfitting.

github-actions[bot] commented 3 hours ago

Thanks for creating the issue in ML-Nexus!🎉 Before you start working on your PR, please make sure to: