Data-Science-Machine-Learning-Projects

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Data-Science-Machine-Learning-Projects

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This Repository Contains different Machine Learning Projects on various dataset. From Exploratory Data Analysis - Visualization to Prediction and Classification..

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# Machine-Learning-Classification-And-Regression-Projects
This Repository Contains different Machine Learning Projects on various dataset. From Exploratory Data Analysis - Visualization to Prediction and Classification.

# Breast Cancer Predication

* Dataset used : [Click here to download](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data)
* Here I have uploaded 2 versions of this project. Difference between these two versions is Feature Selection based on correlation.
* Both version contains in-depth insight into Dataset - Exploratory Data Analysis, Visualization and Data Preparation.
* Also tried various methods for data preparation like handle outliers and data imbalance.
* Version 1 : This [Notebook](https://github.com/rkshiyaniya/Data-Science-Machine-Learning-Projects/blob/main/Breast%20Cancer%20Classification%201.ipynb) contains simple method for feature selection based on correlation with target attribute.
* Version 2 : This [Notebook](https://github.com/rkshiyaniya/Data-Science-Machine-Learning-Projects/blob/main/Breast%20Cancer%20Classification%202.ipynb) contains logistic regression method for feature selection based on column's accuracy.
* Able to got ~100% accuracy.

# Red Wine Quality Prediction

* Dataset used : [Click here to download](https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009)
* Notebook contains in-depth insight into Dataset - Exploratory Data Analysis, Visualization and Data Preparation.
* This [Notebook](https://github.com/rkshiyaniya/Data-Science-Machine-Learning-Projects/blob/main/Red%20Wine%20Quality.ipynb) contains simple method for feature selection based on correlation with target attribute.
* Tried different algorithms for classification and got ~98% accuracy.

# Loan Answer Prediction

* Dataset used : 
- Train - [Click here to download](https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv)
- Test - [Click here to download](https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv)
* Notebook contains - Data Preprocessing by Scaling, Transforming into One-hot vectors, Data Preparation for model bulding and Model evaluation.
* This [Notebook](https://github.com/rkshiyaniya/Data-Science-Machine-Learning-Projects/blob/main/Proj_Loan.ipynb) contains end-to-end implementation of the projects and used different Classification algorithms.

# Customer Segmentation - K-means

* Dataset used : [Click here to download](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/labs/customer_segmentation.csv)
* This [Notebook](https://github.com/rkshiyaniya/Data-Science-Machine-Learning-Projects/blob/main/Clustering_k_means.ipynb) contains in-depth explaination of K-means clustering algorithm with it's working visualization on randomly generated dataset.
* Also, Used K-means to segment customers to 3 different Clusters.

# Patient Drugs Prediction

* Dataset used : [Click here to download](https://github.com/rkshiyaniya/Data-Science-Machine-Learning-Projects/blob/main/drug200.csv)
* Notebook contains end-to-end implementation of Decision Tree Classifier with printing tree also.
* Tree : [Click here to view](https://github.com/rkshiyaniya/Data-Science-Machine-Learning-Projects/blob/main/drugtree.png)
* This [Notebook](https://github.com/rkshiyaniya/Data-Science-Machine-Learning-Projects/blob/main/Decision_Trees_drugs.ipynb) contains data preprocessing, model building and model evaluation.
* Got ~98%+ accuracy.

# Nutrition Facts for McDonald's Menu Analysis

* Dataset used : [Click here to download](https://www.kaggle.com/mcdonalds/nutrition-facts)
* This [Notebook](https://github.com/rkshiyaniya/Data-Science-Machine-Learning-Projects/blob/main/McDonald's%20Menu.ipynb) contains Exploratory Data Analysis and Visualization about Nutritions in McDonald's Menu

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