This repository contains the projects that I made in the Python programming language.
--> Python is a high-level, general-purpose, and very popular programming language.
--> Python programming language (latest Python 3) is being used in web development, Machine Learning applications, along with all cutting-edge technology in Software Industry.
--> Python is available across widely used platforms like Windows, Linux, and macOS.
--> The biggest strength of Python is huge collection of standard library.
--> Colaboratory, or “Colab” for short, is a product from Google Research which allows anybody to write and execute python code in Jupyter notebook through the browser.
--> Visit colab at:
--> Create account using google account.
--> Once account creation is done, we can directly start coding in colab.
--> It supports Python and R.
--> Files are directly saved in Google Drive.
--> To install python library this command is used-
pip install library_name
Complete Description about the project and resources used.
--> Data Visualization is the presentation of data in pictorial format.
--> Target was to see which automobile gives the most features and variations using data visualization.
--> In this project visualization of CSV file containing data of automobiles is done in python.
--> Data visualization is done to analyze which body-style of car gives the most features.
--> Patterns found in the analysis are listed.
--> Dataset is taken from: 🔗
--> This contains data about various automobile in Comma Separated Value (CSV) format.
--> CSV file contains the details of automobile-mileage,length,body-style among other attributes.
--> It contains the following dimensions-[60 rows X 6 columns].
--> The csv file is already preprocessed ,thus their is no need for data cleaning.
--> Data Visualization is the presentation of data in pictorial format.
--> Target was to see the features and variations of this dataset and the best data visualization technique for this dataset.
--> In this project visualization of CSV file containing data of new york taxi trip is done in python.
--> Data analysis is done to decide suitable data visulization technique.
--> Finally Data Visualization is done to represent the analysis in an understandable way.
--> Dataset is taken from: 
--> This contains data about various NBA Players in Comma Separated Value (CSV) format.
--> CSV file contains the details of players-height,weight,team,position among other attributes.
--> It contains the following dimensions-[457 rows X 9 columns].
--> The csv file is already preprocessed ,thus their is no need for data cleaning.
--> This Contains a simple and interactive implementation of rock, paper and scissors game.
--> Color code and emojis are also added.
--> This contains a simple scratch implementation of background remover in python.
--> Using this we can detect face from images and remove background.
--> Data Visualization is the presentation of data in pictorial format.
--> Data Cleaning and Visualization is done for this dataset.
Dataset is taken from: 🔗
--> Data Visualization is the presentation of data in pictorial format.
--> Data Cleaning and Visualization is done for this dataset.
--> This Project contains code for EDA and Data Visualization of Google App store data.
--> All findings are summarized, code is also explained.
--> At the end of this project, I have explained all findings in brief.
--> After all graphs explanation is also given to understand the results.
--> Objective: Develop a machine learning system for email spam classification using Logistic Regression, focusing on accuracy and efficiency.
--> Preprocessing & Feature Engineering: Text data is preprocessed using TF-IDF vectorization, converting raw email text into meaningful features for the model.<br
--> Model: Logistic Regression is used for binary classification, achieving 94.32% accuracy, with strong precision and recall metrics.
--> Deployment: The model is deployed in a Streamlit web app, enabling users to input email text and receive real-time spam classification results.
Short Description about all libraries used in Project.
- Pandas (Panel Data/ Python Data Analysis) - This library is mostly used for analyzing, cleaning, exploring, and manipulating data.
- Matplotlib - It is a data visualization and graphical plotting library.
- Seaborn - It is an extension of Matplotlib library used to create more attractive and informative statistical graphics.
Drop a 🌟 if you find this repository useful.
If you have any doubts or suggestions, feel free to reach me.
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