shraiyya / career-launcher-internship

Career-Launcher-Internship

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Artificial Intelligence and Machine learning are one of the fastest growing fields in the world today.Machine Learning means machines should be able to learn and adapt through experience .The Machine Learning Internship offered by Career Launcher was a 8 week long internship consisting of 7 modules. The data science problem was a a real life financial world issue related to stocks.The students could choose an industry which was related to discipline or any industry of their liking. On doing so, stock data of a company belonging to the industry chosen had to be downloaded as a csv file.The stock which I used was Hindustan Unilever (HUL).

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The modules were as follows:

• Module 1- Data Exploration Using Pandas

The tasks include ploting the daily volumes of a given stock ,comparing the percentage stem plot to it,documenting your analysis of the relationship between volume and daily percentage change , finding VWAP etc.

• Module 2- Plotting in Financial Markets

The tasks included analysing the correlation between daily returns of any 5 stocks of your liking,stem plots to see daily change,the use of bollinger bands etc.

• Module 3- Regression - Beta Calculation

The tasks included ploting the appropriate trade calls for the given stock data by useing 21 day and 34 day moving averages to get the appropriate predictions for the calls, CAPM calculations,beta calculation,comparing the given stock with gold prices etc.

• Module 4- Algo Trading using Classification

The tasks included creation of the call column,training the model using various machine learning classifiers,plotting the cummalative returns in percent etc.

• Module 5- Modern Portfolio Theory

The tasks included making a portfolio using 4 different stocks ,calculation of the volatility and mean expected annual return,preparing a scatter plot for differing weights of the individual stocks etc.

• Module 6- Clustering for Diverse portfolio analysis

The tasks included making a portfolio using 30 different stocks ,calculation of the volatility, using K Means clustering to classify each data point into a specific group etc.

• Module 7- Internship Report Finalization

Created a final internship report on the work done.

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1. Jupyter notebook was used to write and execute python code, display the output and display visualization plot.

2. Programming was done in Python and there was usage of machine learning libraries from Scikit-Learn (e.g., Pandas, Matplotlib, Seaborn etc).

3. Implementation of machine learning algorithms for Regression and Classification took place.

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