m-imani Goto Github PK

alva-insights

analyzing-stock-market

Using yfinance and webscraping to extract Tesla Revenue Data and GME Revenue Data, then display this data in graphs.

chicago-socioeconomic-indicators

Store a real world data set from the internet in a database (Db2 on IBM Cloud), gain insights into data using SQL queries, visualize a portion of the data in the database to see what story it tells.

classification-methods

load a dataset using Pandas and apply the following classification methods (KNN, Decision Tree, SVM, and Logistic Regression) to find the best one by accuracy evaluation methods (Jaccard, F1-score, LogLoss) for this specific dataset.

exploring-toronto-neighborhoods

gradientdescent-octave

Implement linear regression with one variable to predict profits for a food truck using gradient descent algorithm in Octave.

logistic-regression-octave

Building a logistic regression model to predict whether a student gets admitted into a university in Octave

models-development

developing several models (Linear Regression, Multiple Linear Regression, and Polynomial Regression) that will predict the price of the car using the variables or features. Then evaluating these models (in-sample, and cross-validation) using R-squared and Mean-Squared-Error metrics to find out which model is a better fit for this dataset.

neuralnetwork-polynomial-lr

In this Notebook, MSE of three models (LinearRegression, Polynomial, and 3-layer Neural Network using Keras) has calculated and compared

predicting-bike-rentals

Apply decision trees and random forests to predict the number of bike rentals.

predicting-car-prices

Predicting a car's market price using its attributes by the help of several Python's libraries including: pandas, numpy, skleran, and KNN classifier.

predicting-house-prices

Working with housing data for the city of Ames, Iowa, United States from 2006 to 2010 and then try to predict houses prices using pandas, numpy, sklearn and linear regression.

predicting-loan

Loan prediction using Random Forest, Decision tree, SMOTE and SMOTETOMEK techniques.

pyspark-steam-gaming

Working with Steam Gaming datasets in PySpark to Find day and hour when most new accounts were created.

supervised-learning

This notebook is about creating a 2D dataset and using supervised machine learning algorithms like K-Nearest Neighbor, Support Vector Machine and Linear Regression to classify data points then selecting the best parameters using cross validation method, and finally comparing the results.

unsupervised_learning

This notebook is about creating a 2D dataset and using unsupervised machine learning algorithms like kmeans, kmeans++, and Agglomerative Hierarchical clustering methods to classify data points, and finally comparing the results.