Data-cleaning car models project | Python

Data cleaning project focused on an unclean dataset containing information on the resale value of of cars from UK's Craigslist. I used the Pandas and NumPy libraries to apply methods for dealing with messy data, missing values, inconsistent formatting, malformed records, and nonsensical outliers.

Seoul Bike Sharing Demand Data Visualization | Tableau

The Seoul Bike Sharing Demanding Data Set was obtained for research carried out and published by Sathishkumar V, Jangwoo Park, and Yongyun Cho in 2020. They employed data mining techniques for bike sharing demand prediction in Seoul city in 2018 (Sathishkumar et al., 2020; Sathishkumar & Cho, 2020).

Bike Stores Sample Database | Microsoft SQL Server

SQL programming is used in Microsoft SQL Server for the "Bike Store" database. We tracked several products catalog, inventory, customers, sales, and employees. I implemented queries, retrieve, and aggregate thousands of records to gain insights. Additionally, the OLAP cubes for advanced analytics.

Data Cleaning Flickr Images Books | Python

Dropping unnecessary columns from a DataFrame, changing its index, using .str() methods to clean columns, applying the DataFrame.applymap() function to clean the entire dataset element-wise, renaming columns to more recognizable labels, and skipping unnecessary rows in a CSV file.

Metro Interstate Traffic Volumen | R

I will be analyzing how changes in the climate can affect the traffic volume for westbound interstate I-94 from 2012-2018. Then we carry out a multilinear regression model to predict how temperature, raining and snow could impact the traffic volume.

SVM classifier with GridSearchCV | Python

I will introduce you to a machine learning project on employee attrition prediction using Python. The aim is to predict whether an employee will stay or leave their job. To do this, I employed a Support Vector Machine (SVM) classifier model with GridSearchCV, Confusion Matrix, and Precision/Recall

Developing a higher-performance model based on Deep Neural Network to predict CO2 Emissions in Canadian Vehicles from 2017 - 2023

Predicting the outcome of CO2 emissions based on to built and training a deep neural network model in vehicles tested from 2017 to 2022 in Canada using an open dataset. Follow the GitHub Link