Data-Science-Portfolio_Vasu-Kumar

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Data-Science-Portfolio_Vasu-Kumar

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Repository containing portfolio of data science projects completed by me for academic, self learning, and hobby purposes. Presented in the form of iPython Notebooks, and R files. *Note: Data used in the projects (accessed under data directory) is for demonstration purposes only.

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## Data-Science-Portfolio-VASU_KUMAR

This Portfolio is a compilation of all the Data Science and Data Analysis projects I have done for academic, self-learning and hobby purposes. It also includes my skills and my MOOC certificates. This portfolio is updated on the regular basis.

Email: vasukumardce@gmail.com

LinkedIn: https://www.linkedin.com/in/vasukumar92/

Website: https://vasukumar6.wixsite.com/mysite

*Note: Data used in the projects is for demonstration purposes only.

## Contents

## Projects:

- **Deep Learning Using Tensorflow and Keras**

- [Improvise a Jazz Solo with an LSTM Network](https://github.com/vasukumar92/Improvise-a-Jazz-Solo-with-a-LSTM-Network/blob/master/Improvising%20a%20Jazz%20Solo%20with%20a%20LSTM%20Network.ipynb) : This notebook discusses a model that uses an LSTM to generate music. I applied an LSTM to music generation and Generating jazz music was done with deep learning.
     
     - [Trigger Word Detection for a Laptop using Deep Learning](https://github.com/vasukumar92/Trigger-Word-Detection-for-a-Laptop-using-Deep-Learning/blob/master/Trigger%20Word%20Detection%20using%20Deep%20Learning%20(1).ipynb) : In this notebook, we will construct a speech dataset and implement an algorithm for trigger word detection (sometimes also called keyword detection, or wakeword detection). Trigger word detection is the technology that allows devices like Amazon Alexa, Google Home, Apple Siri, and Baidu DuerOS to wake up upon hearing a certain word. For this exercise, our trigger word will be "Activate." Every time it hears you say "activate," it will make a "chiming" sound. By the end of this assignment, you will be able to record a clip of yourself talking, and have the algorithm trigger a chime when it detects you saying "activate."

- [Autonomous driving Car detection](https://github.com/vasukumar92/Autonomous-driving---Car-detection/blob/master/Autonomous%2Bdriving%2Bapplication%2B-%2BCar%2Bdetection%2B-%2Bv3.ipynb) : The notebook presents the AI based object detection using the very powerful YOLO model to detect cars, people and other objects. Filtering with a threshold on class scores was done.
     
     - [Face-Recognition for a Guest House](https://github.com/vasukumar92/Face-Recognition-for-a-Guest-House/blob/master/Face%20Recognition%20Model%20for%20a%20House.ipynb): A Face Recognition Siamese Network implemented using Keras. Siamese Network is used for one shot learning which do not require extensive training samples for image recognition.
     
     - [Deep-Learning and LSTM-Models for Delivery date and Cost Prediction](https://github.com/vasukumar92/Deep-Learning-and-LSTM-Models-for-Delivery-date-and-Cost-Prediction/blob/master/Deep%20Learning%20and%20LSTM%20Models%20for%20Delivery%20date%20and%20Cost%20Prediction%20(1).ipynb) : The objective of the project is to obtain the prediction of delivery date and freight cost based on the historic trend and attributes.The notebook showcases the exploratory data analysis and Deep learning model for delivery date prediction as well as LSTM model for freight cost prediction.

- [USD-INR conversionrate prediction Deep-Learning LSTM-Models](https://github.com/vasukumar92/USD-INR-conversion-rate-prediction-Deep-Learning---LSTM-Models/blob/master/USD%20INR%20conversion%20rate%20prediction%20Deep%20Learning%20-%20LSTM%20Models%20(1).ipynb) : The objective of the python notebook is to present the steps done towards analysing the time series data of USD to INR right from 1980 to 2017. The models presented here are SVR, Deep Learning models, and LSTM Model.

- **Machine Learning**

- [Ensemble-Modeling Rare Event Classification](https://github.com/vasukumar92/Ensemble-Modeling-Rare-Event-Classification/blob/master/Ensemble%20Modelling%20incorporating%20Random%20Undersampling.ipynb) : Rare Event Classification (Ensemble Modelling incorporating Random Under-sampling). The data consists of 10,500 credit applications, each classified as good or bad credit. However, there are only 500 bad credit applications. Since this is less than 5% of the data, classifying applicants as bad credit is referred to as a rare event problem. This is also known as anomaly dete ction in many applications. Approach: The best ratio is discovered by trying ratios between 50:50 to 85:15. Build an ensemble model based on the optimum ratio selected. This is done my creating ensemble of trees using the optimum ratio, fitting a model to each, making classification probability predictions for each and then averaging those to get predicted classification probabilities. From that we can calculate the loss totaled over all the trees. The base model is a decision tree with a minimum leaf size is 5, and the minimum split size is 5. The optimum depth for this model is determined by optimizing the F1-score using 10-fold cross-validation.
     
     - [Drone Flight Data Anlaysis and Flight Range Prediciton using Neural Networks](https://github.com/vasukumar92/Drone-Flight-Data-Anlaysis-and-Flight-Range-Prediciton-using-Neural-Networks/blob/master/ZIPLINE.ipynb): Understanding of the flight condition, performance, efficiency is a vital part of a company’s strategy. Analyzing and studying flight details can help a company to direct the use of right type of product at right location/conditions at right time. In this report we would be leveraging Data Analysis techniques to study the flight details, launches, to target the right parts and perform EDA of flight status.
     
     - [Machine-Learning based Root cause analytics and prediction](https://github.com/vasukumar92/Machine-Learning-based-Root-cause-analytics-and-prediction/blob/master/Root%20Cause%20Analytics%20and%20Prediction%20(1).ipynb)The objective of the project is to analyse root causes of the late deliveries by a vendor.

- [Natural Language Processing using NLTK on eBooks](https://github.com/vasukumar92/Natural-Language-Processing-using-NLTK-on-eBooks/blob/master/NLTK_Books.ipynb) : There are 8 different text files of ebooks which are available freely on http://www.gutenberg.org/ . Steps Performed: Importing of text files to python, Text Parsing and transformation operations are performed such as lower case conversion, removal of special characters, contraction words, tokenizing etc., Tagging parts of speech to each term, Stemming terms to get their root word, Stop Word Removal. The project also shows the difference in the outcome when POS Tagging, Stop Word Removal and Stemming operations are not performed.

- [Prediction Analysis of Risky Credit for a credit card company using Neural Networks and Random Forests](https://github.com/vasukumar92/Prediction-Analysis-of-Risky-Credit-ANN-RF-DT-/blob/master/Credit%20History%20_Risk%20(1).ipynb) : Developed a model to determine the credit risk for a customer based on the attributes. Performed data imputations using one-hot method. The Random Forest model results the accuracy of 78%, Recall of 93.24%, precision of 80.23% and F1 score of 86.25%. The training model for Random forest however shows 95.71% Accuracy, which shows overfitting of curve. Hence we rely on neural network with Neural Network - 6,5.

- [Recommender system for Groceries Contractor](https://github.com/vasukumar92/Recommender-System-for-Groceries-Contractor/blob/master/Groceries%20Recommender%20System%20(1).ipynb) : Determined the highest restaurant-joints cluster center (at Agincourt) for the groceries contractor in one of the boroughs of Toronto (Scarborough). Produced folium maps visualization

- [Loan Default predictive modelling for a customer of a Multinational Bank](https://github.com/vasukumar92/Loan-Default-Prediction/blob/master/Loan%20Default%20Classifier.ipynb) : A classification model to accurately detect if a customer is going to default the loan/ credit payments. The goal is to understand the important factors affecting the loan status and building an optimized classification model. I have used kNN, SVM, Decision Tree and Logistic regression algorithm.

- [Mine-Rock classifier based on Sonar data inputs](https://github.com/vasukumar92/Mine-vs-Rock-Classifier---SONAR-Data/blob/master/Mine%20Sonar%20Classifier.ipynb) : I have used a sonar data inputs (signals bouncing off from metal and rock surfaces). I have used analysis for outlier and missing value identification and imputation.  A classification model is built to detect if the signals contribute or predict the presence of a Mine or a Rock.

- [Customer Churn Analysis for a Telecommunications Company](https://github.com/vasukumar92/Customer-Churn-Analysis-for-a-Telecommunications-Company/blob/master/Customer%20Churn%20Analysis%20for%20a%20Telecommunications%20Company.ipynb) : I have used a telecommunications dataset for predicting customer churn. This is a historical customer dataset where each row represents one customer. The data is relatively easy to understand, and you may uncover insights that can be used immediately. Typically it is less expensive to keep customers than acquire new ones, so the focus of this analysis is to predict the customers who will stay with the company.

- [Data Analytics of Diamond Pricing and its associated features](https://github.com/vasukumar92/Data-Analytics-of-Diamond-Prices-and-Features/blob/master/Data%20Preprocessing%20(1).ipynb) : I have built a linear regression model in order to accurately predict the diamond prices based on the features. The features are Price, Carat, Cut, Color, Clarity, Depth, Tabl, X, Y, and Z dimensions. The Data consists of many missing values which are assessed and analysed in order to proceed for model building. Objectives included Metadata Preparation, Outlier Analysis, Missing Value Imputation, New Attribute Creation and Attribute Encoding.

- Data Analytics and exploration of Diamond pricing [SAS] : Performed outlier analysis, missing value imputation, attribute encoding, determined important features responsible for diamond pricing. Performed forward -backward and stepwise linear regressions to predict the diamond pricing
    
    - Prediction of the file claim for an insured car [R] : Achieved 84% accuracy with sensitivity of 73%. Analyzed 1,00,000 observations and 52 factors with 6.2% missing values. 12 Important features selected (Subset selection, Ridge and LASSO)

- Movie recommender system using collaborative filtering : Developed user-based recommendation system using Pearson correlation function. Recommendation given based on the similarity scores

- **Data Analysis and Visualization**
    - [Data Analysis of Immigration Data for Canada](https://github.com/vasukumar92/Immigration-Data---Visual-Analytics/blob/master/Immigration%20Data%20-%20Visualization%20Analytics%20(2).ipynb) : I have performed data cleaning and used visual plots to represent immigration to Canada by continents over different time periods, understand Japanese, Chinese and Indian immigrants from 1980-2013, regression plots to understand the immigration pattern, and Analyzing the effect of Argentina's great depression.
    
     - [Advanced Visualizations for Immigration Data and Novel](https://github.com/vasukumar92/Advanced-Data-Visualizations---Immigration-Data-and-Novel/blob/master/Advanced%20Visual%20Analytics%20-%20Immigration%20Data%20and%20Novel.ipynb) : The objective of the project is the exploration of datasets, Visualizing Data (Waffle Charts, Word Clouds, Regression Plots). Assessment of Denmark, Sweden and Norway for total immigration is shown via waffle plot. The project shows the word cloud along with masking with frequent words occurrence (excluding non important words). Also, the majority of the people who immigrated came from one of 15 countries that are displayed by the word cloud. The regression plots discuss the immigration pattern over the years.    
    
- **Micro Projects**
   
   - **[AI Chatbot Developement :](https://vasukumar.intelaedu.com/)** The badge earner has demonstrated an understanding of the creation of Chatbots by leveraging Watson Conversation and their deployment on WordPress.

- **Phase 1 Analysis of Multivariate Quality Control Data for an Industrial Forging Process :** Estimated distribution parameters from within control data point to use it as monitoring scheme for future quality control missions (Phase 2 Analysis).
    
    - **Prediction of Network Intrusion detection by analyzing the TCP/IP dump :** Achieved 75% accuracy with the Random Forest/ Decision Tree model. Analyzed 25193 records along with data cleaning and feature selection of TCP/IP Dump data
    
    - **E-Commerce Sales Analytics Visualization [Tableau] :** Performed data organization, advanced visualization, mapping, enterprise dashboards, and data presentation
    
## Skills: 
    - Programming Languages: PYTHON, R, SAS, C++, MATLAB, Minitab, Shell Scripting, Java, Apache Spark
    - Databases: mySQL, IBM Db2, NoSQL(Redis, MongoDB), Oracle
    - Optimization Problem Solving : AMPL /CPLEX
    - Tools: Git, AWS, IBM Watson, Hadoop, MapReduce, Googlecloud
    - Machine Learning Skills: Bayesian Methods, Support Vector Machines, Regression Models, Graphical Models,Deep Learning, Clustering, LDA, QDA, Natural Language Processing, Feature Engineering , Neural Networks, Artificial Intelligence,  CNNs, RNNs, and LSTMs

## Certifications:

### DeepLearning.ai INSTITUTE: Deep Learning Specialization

- Deep Neural Networks(IN PROGRESS)
 - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (IN PROGRESS)
 - Convonutional NeuraL Networks (IN PROGRESS)

### IBM DIGITAL BADGE FOR ADVANCED DATA SCIENCE DEGREE

- Applied AI with Deep Learning (IN PROGRESS)

### IBM DIGITAL BADGE FOR DATA SCIENCE PROFESSIONAL DEGREE

- [IBM Data Science Professional Certificate](https://www.youracclaim.com/badges/6036b3d3-7179-49cb-977d-0bff1d71eec9/linked_in)

- [Databases and SQL for Data Science](https://www.coursera.org/account/accomplishments/verify/5ZUCBSV8MTUY)
 
 - [Machine Learning with Python](https://www.coursera.org/account/accomplishments/verify/9F9333ZFFZS8)

- [Data Visualization with Python](https://www.coursera.org/account/accomplishments/verify/MBSRXL7HPYZK)

- [Data Analysis with Python](https://www.coursera.org/account/accomplishments/verify/3YZ6EX5Q2QMH)
    
### AI Chatbot Development
 - [Build Your Own Chatbot by IBM](https://www.youracclaim.com/badges/687770ea-aaa9-4b08-9b93-74342b231b73/linked_in_profile)
 
### Stanford :

- [Statistical Learning](https://verify.lagunita.stanford.edu/SOA/bef05e882c8947549c075d3783bb6a32/)
    
 #### [Click here to go to my Google Scholar Profile](https://scholar.google.com/citations?user=KChhjggAAAAJ&hl=en): Citations- 41, h-index-4
 
 #### [Click here to go to my Researchgate Profile](https://www.researchgate.net/profile/Vasu_Kumar2): RG Score- 5.17

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