Machine Learning Projects

Overview

This consists of various machine learning projects in which each projects was done as end to end projects which means from Data Collection through feature engineering, feature selecion to Deployment and Maintainance. The whole app was built with Flask framework. You can launch my app by clicking here.

For building machine learning models, I have used scikit-learn alias sklearn.

The model was deployed on Heroku platform which is a platform-as-a-service.

Motivation

While most of the students like me spending their precious time on PUBG, I decided to spend my precious time on learning to gear up my knowledge and my life. During this lockdown period I have learnt countless things related to Data Science and Machine Learning such as Data Preprocesing, Exploratory Data Analysis using NumPy and Pandas, Feature Engineering, Feature Selection, Feature Scaling, Visualization Techniques using Matplotlib and Seaborn and various Machine Learning Algorithms from scratch along with Hyper Parameter Tuning. Later I want to do some projects for the society with my knowledge. So I have started with some beginner level projects to learn some more data related concepts and techniques and step on to some advanced level projects while progressing.

I always love to share my knowledge with my friends and helps on solving their coding related problems with my best and they also help me on learning new things and keeps motivating me. Though it is a small range of community, I want to share my knowledge to everyone. Whenever I wants relaxation, I write blogs to stay relevant. You can see all my blogs by clicking here.

Technical Aspects

Since we can deploy atmost 5 app in Heroku as a free service, I came up with a new idea of deploying all my projects in a single web app and controlling the each projects navigation with Flask and Python.

Some extra-ordinary features that I have included in my app :

Visualizing Probability of classifcation in each classification type problems.
Added all the details about the projects such as data source, code source, libraries and frameworks used in each project's description.
All my end to end projects in a single view.

Installation

To run my app on your local machine, do the following steps.

Step 1 :

I have written the Code with Python 3.7.7. If you don't have Python installed you can find it here.
If you are using a lower version of Python you can upgrade using the pip package, kindly ensure that you have the latest version of pip.

Step 2 :

If you want the current version of my repository to be in your github, you can do forking my repository by clicking as shown in the picture below.

Clone my repository to your local machine by running the following command. Before doing this, you have to install git on your machine and make sure you are having proper internet connection.
- For Windows OS user, open git bash and run the following command.
```
git clone https://github.com/Kisalaykisu/Machine-Learning-Projects.git
```
- For Linus OS user, open Terminal and run the following command.
```
git clone https://github.com/Kisalaykisu/Machine-Learning-Projects.git
```
If you don't want to mess up with all these things, you can just download the zip file of my GitHub repository by clicking [here] and extract it to any filelocation as your wish and then use it.
Now we have done with the downloading of my whole project.

Step 3 :

After downloading the whole repo, get into the main folder by hit the following command in git bash for Windows OS users and Terminal for Linux OS users.

cd Machine-Learning-Projects

Step 4 :

Now we are going to install all the dependency libraries for this project. Before that you must have Python 3.7.7 and latest version of pip.
To install all the dependency libraries in a single command, run the following command.

pip install -r requirements.txt

Step 5 :

After installing all the dependency libraries, you are ready to run my app on your local machine.
To launch my app on your local machine, hit the following command.

python ml_projects.py

Run

Now you have successfully launched my app on your local machine.
To view my app, hit the following URL in any of the browser such as Chrome, FireFox, etc..,
```
http://127.0.0.1:3000/
```

Deployment on Heroku

After successfully running my app on your local machine, you can also deploy my app on heroku by following the steps given on Heroku Documentation. You have to do it on your own risk.
In case, if you have any issues on deploying the app, please open a new issue by clicking here. Because it may help others while deploying.

Directory Tree

Machine-Learning-Projects
├── 01.Iris_Species_Classification
├── 02.Loan_Status_Prediction
├── 03.Gender_Classifcation
├── 04.Weight_Prediction
├── 05.Boston_House_Price_Prediction
├── data
│   ├── ML_Projects.csv
│   └── social-media-links.csv
├── Demos
├── models
├── py_modules
│   ├── __pycache__
│   └── data_extraction.py
├── static
│   ├── css
│   ├── files
│   ├── images
│   └── js
├── templates
│   ├── base.html
│   ├── all_projects.html
│   ├── weight_predictor.html
│   ├── gender_classifier.html
│   ├── iris_species_classifier.html
│   ├── loan_status_predictor.html
│   └── boston_house_price_predictor.html
├── LICENSE.txt
├── ml_projects.py
├── Procfile
├── requirements.txt
├── runtime.txt
└── README.md

To Do

These features will be added in the future :

Connecting my portfolio app with this project app very soon.
Using Exception handling for handling invalid input.
Adding a link to the data source in the project description popup modal.
Adding GitHub link to each projects in both description popup modal and project page.
Adding better visualization charts for classification type problems.
Adding visualization for regrerssion type problems.

Bug / Feature Request

If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result.

If you'd like to request a new function, feel free to do so by opening an issue here. Please include sample queries and their corresponding results.

Technologies Used

Credits

Kaggle - I have collected dataset from kaggle for some of the projects such as Loan Status Prediction, Iris Species Classification, Boston House Price Prediction, etc..,

Kisalaykisu/Machine-Learning-Projects