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This repository contains an exploratory data analysis (EDA) project focused on roller coasters. The project involved organizing, cleaning, and visualizing the data to gain insights into roller coasters' characteristics and performance.
The goal of this project is to perform Exploratory Data Analysis (EDA) on a dataset using Python tools and libraries within a Jupyter Notebook environment.
The Goodreads data set consists of information on over 11000 books, including title, author, publisher, rating, and review information. This notebook is exploratory data analysis of the data from the Goodreads dataset.
EDA on Spotify Top 50 Dataset. More than focusing on EDA in this I have focused on using visualizations better and adding more parameters to make them more refined and readable. More of a notebook to understand commonly used plots better and use available parameters to make the most of these plots.
I chose a dataset from kaggle and performed an EDA, over 30 insights (visualization) was produced. The key insights is presented in a Jupyter Notebook slide..
Assume the role of a growth analyst at Mercado Libre. Produce a Jupyter notebook that contains your data preparation, your analysis, and your visualizations for all the time series data that the company needs to understand.
Analyze market research data to uncover key insights into financial behaviors across demographic segments. This Jupyter Notebook explores age, social class, and gender correlations, delivers compelling visualizations, and validates findings with statistical tests. A real-world project designed to guide targeted financial services.
This is notebook where I just create random idenitity and Autoencoder networks to actually see what the convolution layers are learning. Mostly I visualise the internal layers activation and conclusions can be drawn about what the hidden layers are learning. For anyone who is trying to build CNN. This can also help them guide them into thinking …