Machine Learning Portfolio In this portfolio, repositories contain machine learning projects completed by me for academic purpose , enthusiastic of self-learning and continuous interest. This portfolio is presented in Co-lab notebooks(hosted by Google) and as well as in python version. All academic works are done by me and from the sources that …
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Artificial Neural Network_ANN
California_Big_Housing
ELO Merchant Category Recommendation ELO_fix_readme1.1 Dec 31, 2018
Face Detection by Image
Face Detection by Video update1.13 Nov 28, 2018
Facial Keypoints Detection
LSTM Google Stock Price update Content1.2 Dec 18, 2018
Pandas
Python Review
Stock Market Analysis (Uniqlo) update Contents folder Dec 18, 2018
Titanic
Twitter Sentiment Analysis
data first commit Nov 28, 2018
README.md

README.md

Machine Learning Portfolio

In this portfolio, repositories contain machine learning projects completed by me for academic purpose , enthusiastic of self-learning and continuous interest. This portfolio is presented in Co-Lab notebooks(hosted by Google) and as well as in python version.

All academic works are done by me and from the sources that I learned from mlacademy.io /acknowledged to Venkatesh Tadinada.

Note: Data that used in this portfolio is for academic and demonstration purpose ONLY!!!

Contents

California Housing Price

Compared income category proportion in Stratified Sampling and Random Sampling. Visualized the data to gain insights with Matplotlib. Processed the categorical input feature to a one-hot vector and took care of missing values. Analyzed this end to end machine learning project with different models.

  • ML problem: House Price Prediction
  • Technologies: Numpy, Pandas, mathplotlib, sklearn
  • Implementation: Google Colab

Google Stock Price

Predicted Google opening stock price based on last 5 year’s data of Google Stock Price using RNN(LSTM) alongside with Keras framework based on the data structure with 60 timesteps with 1 output or based on the trends during the 60 time steps, we predict the next output.

  • ML problem: Stock Price Prediction
  • Alogrithms: RNN(LSTM), Deep Learning, Machine Learning
  • Technologies: Numpy, Pandas, mathplotlib, keras, Tensorflow
  • Data: Google Stock
  • Implementation: Google Colab

Stock Market Analysis (Uniqlo)

Predicted closing price with years daily stock price info of FaastRetailing (Uniqlo) in Tokyo Stock Exchange using Time Series Analysis. Looked at a few ways of analyzing the risk of a stock, based on its pervious performance history. Measured the score with evaluation metrics.

  • ML problem: Stock Market Analysis, Stock Market Prediction, Times Series Analysis
  • Technologies: NumPy, Pandas, Seaborn, Mathplotlib, Scikit-Learn
  • Implementation: Google Colab

Twitter Sentiment Analysis

Processed and cleaned tweets or data Generated and visualized from tweets to extract features from cleaned tweets. Perfromed Text Preprocessing ( Noise Removal, Lexicon Normalization), Text to Features Build models with different classification models such as RandomForest, SVM and detected whether tweets are hate speech or not.

  • ML problem: Twitter Sentiment Analysis, RandomForest, SVM
  • Technologies: NumPy, Pandas, Seaborn, Mathplotlib, Scikit-Learn
  • Implementation: Google Colab

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  • Machine Learning

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 Tools: scikit-learn, Pandas, Seaborn, Matplotlib, numPy

  • Deep Learning

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    Tools: scikit, Matplotlib, numPy, Keras, TensorFlow