Welcome to my Machine Learning Showcase repository! This repository showcases three distinct machine learning projects: Churn Prediction, Handwritten Alphabet Image Recognition, and TMDB Movie Revenue Predictions.
In the Churn Prediction project, I've harnessed the power of machine learning to predict customer churn for businesses. The comprehensive analysis includes insightful visualizations, feature engineering, and model selection. By identifying potential churners, businesses can take proactive measures to retain valuable customers.
- Dive into the detailed
ML_Churn_Prediction.ipynbJupyter Notebook to discover the step-by-step process. - Access the 'data' folder to explore the dataset that was pivotal to our model's insights.
My Handwritten Alphabet Image Recognition project is all about unleashing the potential of image processing techniques. By classifying handwritten alphabet characters using the MNIST dataset, I've showcased the synergy between machine learning and image analysis.
- Take a journey through the
Handwritten_Alphabet_Image_Processing.ipynbNotebook to uncover the magic behind image processing. - Don't miss the 'data' folder, where the MNIST dataset resides, powering our model's accuracy.
The TMDB Movie Revenue Predictions project empowers us to predict movie revenues by delving into insightful feature analysis. From budget to cast, my model leverages machine learning to foresee potential movie revenues.
- Embark on an insightful adventure by delving into the
TMDB_Movie_Revenue_Predictions.ipynbNotebook. - The 'data' folder holds the dataset that breathes life into our movie revenue predictions.
- Python (>=3.6)
- Jupyter Notebook
- Required Python packages mentioned in the project notebooks (install with
pip install -r requirements.txt)
Before diving into machine learning models, every project kicks off with exploratory data analysis (EDA). Through visualizations and statistical insights, I delve into the data's intricacies, uncover patterns, and lay the foundation for informed decisions.
My projects encompass a spectrum of machine learning algorithms, each chosen based on the problem's nuances and objectives. Some of the algorithms featured in my projects include:
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Support Vector Machine (SVM)
- Linear Regression
- Logistic Regression
- AdaBoost
- XGBoost
- Decision Tree
- Random Forest
- And more...
I explore their implementations, analyze their performance, and offer insights into when and why certain algorithms excel in different scenarios.
Each project in this repository has been meticulously crafted to provide a deep understanding of the underlying concepts. The journey of each project follows a consistent path:
- Exploratory Data Analysis: Unveiling insights, understanding data distributions, and preparing for model building.
- Algorithm Selection: Choosing the most suitable machine learning algorithms based on the problem statement.
- Model Building and Evaluation: Implementing algorithms, fine-tuning hyperparameters, and evaluating their performance.
- Visualization: Displaying results, highlighting key takeaways, and providing a holistic view of the project.
- Clone this repository:
git clone https://github.com/KIRANKUMAR7-P/Machine_learning.git - Navigate into each project folder and immerse yourself in the Jupyter Notebooks.
- Run the code cells to relive the analysis and predictions firsthand.
I'm excited to discuss these projects and more. Feel free to connect with me on LinkedIn to engage in meaningful conversations.
This repository is licensed under the MIT License, making it ideal for educational and personal use.
Special thanks to the creators of the datasets that fueled these projects, as well as the libraries that made it all possible.
"Unlocking insights, one algorithm at a time."