This repository showcases the finest collection of all projects based on machine learning, deep learning, computer vision, natural language processing and everything. Indulge in this journey of open source.
The main aim is to provide an efficient and beginner-friendly projects that would help you in mastering the ML/AI algorithms and make you familiar. Turn yourself into pro with all the hands-on that got you covered.
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Fork the repository
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Clone your forked repository using terminal or gitbash.
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Make changes to the cloned repository.
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Add, Commit and Push.
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Then in Github, in your cloned repository find the option to make a pull request.
print("Start contributing for Machine Learning Projects")
- Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed.
- You can only work on issues that have been assigned to you.
- If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.
- If you have modified/added code work, make sure the code compiles before submitting.
- Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
- Do not update the README.md.
| S.no | Project Name | Description |
|---|---|---|
| 1. | Data Analysis Of Meteorological Data | It analyzes the meteorological weather data of last 10yrs from 2006 to 2016 at Finland to check an increase of global warming. |
| 2. | Data Analysis of Student's Performance | To understand how the student's performance (test scores) is affected by the other variables (Gender, Ethnicity, Parental level of education, Lunch, Test preparation course). |
| 3. | Stock Price Predictor | The challenge of this project is to accurately predict the future closing value of a given stock across a given period of time in the future. For this project I have used a Long Short Term Memory networks – usually just called “LSTMs” to predict the closing price of the S&P 500 using a dataset of past prices |
| 4. | Real Time Face Recognition | The objective of the proposed solution is to recognize a face of a person from a trained data set. |
| 5. | Customer Segmentation Analysis Using Clusering | The Customer Segmentation has been done using K Means Clustering. Customer segmentation is the process of dividing customers into groups based on common characteristics so that companies can market to each group effectively and appropriately. |
| 6. | Credit Card Fraud Detection using ML | We have done Exploratory Data Analysis on full data then we have removed outliers using "LocalOutlierFactor", then finally we have used Netral networking technique to predict to train the data and to predict whether the transaction is Fraud or not. |
| 7. | House Sales Price Prediction | In this project we are predicting the house sales price in USA. |
| 8. | Telecom Industry Customer Churn | In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. |
This project follows the MIT LICENSE.
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🎉 🎊 😃 Happy Contributing 😃 🎊 🎉