nazihkalo

Nazih Kalo's repositories

FIFA19-Scouting-Analysis

For this project, I am positioning myself as a scouting agency that uses analytics to, among other things, enhance the discovery of talents and help soccer clubs better understand the dynamics (features) that come into play when determining the value, overall and future potential of a player. I will be utilizing the FIFA Player dataset available on Kaggle and apply various Data Mining techniques to achieve this objective.

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Semantic-Scholar-PySpark

Predicting Citations and building a Knowledge Base using Semantic Scholar's Corpus of 175M+ Research Papers.

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steel_defect_detection

Detecting & Classifying Defects in Steel Manufacturing line using computer vision

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Drought-Forecasting-in-R

Drought Forecasting using Time Series models in R

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Ship_Image_Segmetation

Airbus Ship Detection Challenge on Kaggle: Finding ships on satellite images using CNNs

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url-shortener

Flask App to provide shortened custom URLS for localhost

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newscraper-sentiment-extraction-app

An interactive app that scrapes and analyzes sentiment.

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Reinforcement_Learning_Pacman

Repository Exploring different RL methods for playing Ms-Pacman using openai's Atari environment.

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Apple-Health-Data-Analysis

Analyzing data from Apple Health App in python

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brownie_fund_me

Smart contract application from Freecodecamp :) wagmi

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cryptocompare-api

Jupyter Notebook with examples of useful CryptoCompare API calls

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defi-stake-yield-brownie

Defi staking Dapp

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facebook-friend-graph

Plotting your friend network using Plotly, NetworkX and python-louvain

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fund_me

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nazihkalo.github.io

My portfolio [WIP] check it out -->

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nazihkalo.github.io_old

My personal portfolio page hosted by Github

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Predicting-Loan-Interest-Rates

In this project we will be using the publicly available and Kaggle-popular LendingClub data set to train Linear Regression and Extreme Gradient Descent Boosted Decision Tree models to predict interest rates assigned to loans. First, we will clean and prepare the data. This includes feature removal, feature engineering, and string processing.There are several entries where values have been deleted to simulate dirty data. Then, we will build machine learning models in Python to predict the interest rates assigned to loans. We will evaluate our models' performances using the root mean squared error (RMSE) metric and compare our models' results.