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https://github.com/blacksujit/amazon-ml-challange

This is our submission to the amazon ML challange to the final round
https://github.com/blacksujit/amazon-ml-challange

2024 amazon amazon-ml-challange-2024 amazon-ml-challenge amazon-web-services bigdata core core-machine-learning core-ml data-science data-structures-and-algorithms data-visualization datasets deep-learning deployement-strategy jupyter-notebook machine-learning

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This is our submission to the amazon ML challange to the final round

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# Amazon ML Challenge - Machine Learning Model for Data Classification and Analysis



## Overview



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.



## Dataset Description



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].



## Observations from the Dataset:



Missing values exist in [specific columns].



Data contains outliers in [specific columns].



Categorical data needs encoding for machine learning models.



## Project Workflow



**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



## Missing Data Handling:



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].



## Data Splitting:



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



## Metrics Used:



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].



## Setup Instructions



**Prerequisites**



Python 3.8 or above



**Libraries:**



pandas



numpy



matplotlib



seaborn



scikit-learn



## Installation:



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



## Results:



**Model Performance:**



[Insert specific evaluation metrics and scores]



**Key Insights:**



[Summarize findings, e.g., important features or significant trends].



## Future Improvements



**Feature Engineering:**



Add domain-specific derived features.



**Model Optimization:**



Experiment with advanced techniques like XGBoost, LightGBM, or Neural Networks.



## Deployment:



Package the solution into an API for real-time predictions.



## Contribution



Contributions are welcome! Fork the repository, make changes, and submit a pull request. For questions or suggestions, please open an issue.