Welcome to the Machine Learning Projects repository! This repository contains various projects showcasing different machine learning algorithms implemented in Python. Each project is designed to illustrate a specific algorithm or concept in machine learning, complete with detailed explanations, code, and visualizations.
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📈 Ecommerce Company Data
- In this project, we analyze the data for the ecommerce website and predict how to maximize the profits.
- Tech Used: Pandas, Numpy, Matplotlib, Seaborn, Plotly, Scikit-learn
- Model Used: Linear Regression
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🚢 Titanic Survival Prediction
- In this project, we predict whether someone would survive the Titanic disaster based on given parameters.
- Tech Used: Pandas, Numpy, Matplotlib, Seaborn, Plotly, Scikit-learn
- Model Used: Logistic Regression
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📰 Ads Clicked On Prediction
- We analyze whether someone would click on the ad based on the given information.
- Tech Used: Pandas, Numpy, Matplotlib, Seaborn, Plotly, Scikit-learn
- Model Used: Logistic Regression
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💸 Loan Default Analysis
- We analyze wether someone would default on there loan or not based upon the provided attributes.
- Tech Used: Pandas, Numpy, Matplotlib, Seaborn, Scikit-learn
- Model Used: Decision Tree & Random Forest
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❤🩺 Heart Attack Prediction
- We analyze wether someone would have a heart attact or not based upon different paramters like the cholestorl, exercise anginia
- Tech Used: Pandas, Numpy, Matplotlib, Seaborn, Scikit-learn
- Model Used: All classification model with and without PCA
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MNIST Hand Drawn digits Recoginition
- I have made Number of Deep Convolution Neural Network to analyze and predict the Hand Drawn digits
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Cats vs. Dog Classification Model
- Cats vs dog classification model that is made using the knowlege of Convolutional neural network and Data Augmentation Techniques
- Python 3.x
- Pandas
- Numpy
- Matplotlib
- Seaborn
- Plotly
- Scikit-Learn
- Tensorflow
- Keras
pip install pandas numpy matplotlib plotly seaborn scikit-learn keras tensorflow
git clone https://github.com/ahmedyar7/Machine-Learning-Projects.git
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.