Name		Name	Last commit message	Last commit date
Latest commit History 39 Commits
Capstone Project		Capstone Project
boston-housing		boston-housing
creating_customer_segments		creating_customer_segments
finding_donors		finding_donors
smartcab		smartcab
student_intervention		student_intervention
titanic_survival_exploration		titanic_survival_exploration
Forest_of_tree_importances.py		Forest_of_tree_importances.py
Gradient Boosting vs Adaboost		Gradient Boosting vs Adaboost
Indeed_DS_Answers.docx		Indeed_DS_Answers.docx
OLS_VS_Robust_Regression		OLS_VS_Robust_Regression
Precision_Recall_Curve.py		Precision_Recall_Curve.py
README.md		README.md
ROC_curve_for_Classification_for_label.py		ROC_curve_for_Classification_for_label.py
Salary_Prediction.ipynb		Salary_Prediction.ipynb

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The Repository has all the projects that I completed for my "Udacity Machine Learning Engineer Nanodegree"

Please refer below:

"Capstone Project": Built a stock price predictor - This work involved research in financial data analysis as well as trying out various machine learning techniques such as Polynomial learn Regression, KNN, ARIMA for predicting stock prices. Finally, I have used Linear Regression to predict stock prices one day ,7 days, 14 days, 28 days in future with ‘>=.85’ R-squared score on the test datasets

"boston-housing": Predicting Boston Housing Prices - Evaluated the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. This could be invaluable for someone like a real estate agent who could make use of such information daily.

"creating_customer_segments": Created Customer Segments with Unsupervised learning techniques - K means, Gaussian Mixture Model, Means Shift Clustering, Density Based clustering - I have analyzed the dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.

"smartcab": Implemented Reinforcement Learning based Q-Learning algorithm to train a Smart Cab to take optimal actions in an environment

"student_intervention": Built a Student Intervention System using various Supervised learning models - Decision trees, Logistic Regression, Stochastic Gradient Descent, Multilayer Layer perceptron deep learning model, Ensembled tree based models: Random Forest, Gradient Boosting – The goal for this project is to identify students who might need early intervention before they fail to graduate.

"titanic_survival_exploration" - In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this project, I have explored a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive

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All Kaggle projects under "Machine Learning Engineer" Nanodegree

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