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https://github.com/greatwoman23/personal-analytics-showcase


https://github.com/greatwoman23/personal-analytics-showcase

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# personal-analytics-showcase



Titanic Dataset Visualization with Matplotlib, Numpy and Pandas.



#Task

Explore and visualize the Titanic dataset to uncover insights into passenger demographics, survival rates, and more.



#Description

In this project, I leverage the power of Matplotlib to create a series of visualizations that provide a comprehensive view of the Titanic dataset. The visualizations cover a range of aspects, from passenger class distribution to survival rates based on various factors.



#Installation

To explore the Titanic dataset visualizations, follow these steps:



Navigate to the project directory:

Open the Jupyter Notebook to view code and visualizations:



#Usage

Explore the Matplotlib visualizations in the Jupyter Notebook to gain insights into the Titanic dataset. Run each code cell to see the corresponding visual representation.



#Numpy 



Working with NumPy in Titanic Dataset Analysis

NumPy is an integral part of our Titanic Survival Prediction project, providing powerful tools for efficient data manipulation and numerical operations. Here's how NumPy contributes to our analysis:



Why NumPy?

Data Representation: NumPy's arrays allow us to represent and manipulate data efficiently, providing a foundation for handling the dataset.



Array Operations: Perform essential numerical operations on the dataset, such as calculating averages, medians, and other statistical measures.



Data Cleaning: Utilize NumPy's array functions to clean and preprocess data, handling missing values and ensuring data consistency.



Feature Engineering: Create new features and transform existing ones using NumPy, enabling better predictive modeling.



Logical Indexing: NumPy's boolean indexing is handy for filtering and extracting specific subsets of data based on conditions.



Statistical Analysis: Leverage NumPy functions for in-depth statistical analysis of passenger data.



Saving and Loading Data: Save and load preprocessed data efficiently using NumPy's np.save and np.load functions.



Example Usage:

import numpy as np



# Load Titanic dataset (add code for loading dataset here...)



# Perform NumPy operations for data analysis...

mean_age = np.mean(dataset['Age'])

median_fare = np.median(dataset['Fare'])



# Data cleaning and preprocessing...

cleaned_data = preprocess_data(dataset)



# Save cleaned data

np.save('cleaned_titanic_data.npy', cleaned_data)



# Load cleaned data

loaded_data = np.load('cleaned_titanic_data.npy')



# Analyze and visualize survival rates

# (Add code to use Matplotlib with NumPy for plotting)



# Incorporate NumPy to enhance the data analysis of the Titanic Survival Prediction project!



#PANDAS



#Pandas Tutorial

Welcome to the Pandas Tutorial! 🐼



#Overview

This tutorial is your go-to guide for using Pandas, a powerful Python library for playing with data. Whether you're a beginner or a pro, these examples will help you master the art of data manipulation.



#Quick Start

Install Pandas:



pip install pandas

Run Jupyter Notebook:

Launch the Jupyter notebook or script.

Dive into practical Pandas examples.



#What You'll Learn

Pandas Basics:

Explore the fundamentals of Pandas, Series, and DataFrames.

Sorting Data:

Learn how to sort rows based on specific columns.

Reshaping Tables:

Convert between long and wide formats effortlessly.

Pivot and Pivot Table:

Restructure data for analysis and perform aggregations.

Filtering Data:

Discover the art of data filtering based on conditions.



#Contribute

Found a bug? Want to add more examples? We welcome your contributions! Feel free to report issues or submit pull requests.



#License

This project is licensed under the MIT License.



Happy Pandas-ing! 🚀



#The Core Team

Oluwakemi Helen Deniran

Feel free to reach out for any questions or discussions regarding the Titanic dataset visualizations!