Machine Learning by Examples: Python code examples of different machine learning models using sklearn.
In this project, We have a dataset of client for bank and we are trying to predict whether client will subscribe term deposit or not using different machine learning models.
Also different datasets are included with description to practice with that algorithm and get hands on experience with that.
We are using different feature selection techniques and also each model is designed with back propogation for more accuracy.
- Reading From File using Pandas Python library
- DataPreProcessing
- LinearRegression
- LogisticRegression
- SVM
- KNN
- DecisionTree
- RandomForest
- NaiveBayes
To start with project just follow the few steps
$ git clone https://github.com/keyurr2/machinelearning-solutions.git
$ pip install -r requirements.txt
$ cd into algo folder
$ python svm.py
This will install python libraries required to start with Machine learning like Numpy, Pandas and Sklearn
NOTE: We are using Python 3 in this project.
The first step is to download the anaconda for your system.
Now activate anaconda virtual environment and then launch anaconda navigator. Find below command for this.
$ source anaconda3/bin/activate
$ anaconda-navigator
This will launch a web UI for anaconda from where you can open Spyder IDE to run code examples in this project.
- Keyur Rathod (keyur.rathod1993@gmail.com)
This project is licensed under the MIT License - see the LICENSE.md file for details