This repository provides a solution to the Amazon ML Challenge, which involves building a machine learning model for analyzing and classifying data. The notebook included applies modern machine learning techniques to preprocess data, train models, evaluate performance, and visualize results.
The dataset contains features that represent [add a description of the data based on the notebook]. This includes:
Numerical Features: [List numerical features].
Categorical Features: [List categorical features].
Target Variable: [Description of the target variable].
Missing values exist in [specific columns].
Data contains outliers in [specific columns].
Categorical data needs encoding for machine learning models.
Step 1: Problem Understanding
Objective: Develop a model that predicts [specific task] with high accuracy.
Approach: Use supervised learning techniques for [classification/regression] tasks.
Step 2: Data Preprocessing
Numerical features: Imputed using [median/mean].
Categorical features: Imputed with [most frequent category].
Encoding:
Used [One-Hot Encoding/Label Encoding] for categorical variables.
Scaling:
Standardized numerical features using [standard scaling].
Split dataset into training, validation, and test sets in an 80-10-10 ratio.
Step 3: Exploratory Data Analysis (EDA)
Visualized distributions and relationships using matplotlib and seaborn.
Identified key trends and feature importance.
Step 4: Model Building
Used a [specific model, e.g., Random Forest] for prediction.
Hyperparameter tuning using [Grid Search/Randomized Search].
Step 5: Model Evaluation
Accuracy for classification.
F1-Score for imbalanced datasets.
Mean Squared Error (MSE) for regression.
Step 6: Results Visualization
Plotted feature importance and residuals.
Step 7: Deployment (Optional)
Provided an approach for deploying the model using [Flask/FastAPI].
Prerequisites
Python 3.8 or above
Libraries:
pandas
numpy
matplotlib
seaborn
scikit-learn
Clone the repository:
git clone [repository_url] cd [repository_folder]
Install dependencies:
pip install -r requirements.txt
Open and run the notebook:
jupyter notebook Amazon_ML_Model.ipynb
Model Performance:
[Insert specific evaluation metrics and scores]
Key Insights:
[Summarize findings, e.g., important features or significant trends].
Feature Engineering:
Add domain-specific derived features.
Model Optimization:
Experiment with advanced techniques like XGBoost, LightGBM, or Neural Networks.
Package the solution into an API for real-time predictions.
Contributions are welcome! Fork the repository, make changes, and submit a pull request. For questions or suggestions, please open an issue.