pickling

Repository to store sample python programs. Python's simple, easy to learn syntax emphasizes readability.

Simple script to access password information in /etc/passwd

object serialization. ... “Pickling” is the process whereby a Python object hierarchy is converted into a byte stream, and “unpickling” is the inverse operation, whereby a byte stream is converted back into an object hierarchy.

A recommended system for books using popularity and Colaborative filter.

AI-powered agricultural credit analysis system designed to assist in crop financing decisions using predictive analytics and data-driven insights.

Multiple Linear Regression involves having multiple independent features and one dependent feature.

FlameCast is a Flask web application that solves the problem of predicting the Fire Weather Index (FWI) for wildfire risk assessment using a machine learning model. It features a user-friendly interface for inputting meteorological data and instantly viewing the prediction.

A guessing game that scrapes quotes from a website and displays a random quote to the user; the user must guess the quote.

This is a ML (Machine Learning) project for predicting energy consuming by pepole

In this assignment we are tasked with creating demonstration examples of error handling within Python and pickling data. The format of this assignment is different than previous assignments in that this week we are asked to create the examples and demonstrations ourselves. This is much more open ended than previous weeks and it was on us to deci…

A CLI application that writes a to-do list into a binary file and reads a previously saved binary file back to user for updating.

This model predicts the forest fire by using the Ridge-Regression algorithm.

Python library for encapsulating a function definition inside a temporary module file (mostly for use with multiprocessing and Jupyter Notebook).

Analyzing Yelp reviews using textblob to score subjectivity, nltk to predict whether the review is bad, good, or in the middle.

Comprehensive Python programming repository featuring problem-solving, automation scripts, data structures, algorithms, and practical project implementations.

Developed a Bengaluru house price prediction project using Python libraries like Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn. Implemented data cleaning, feature engineering, and model evaluation with techniques such as median imputation, outlier removal, and Random Forest modeling (R-square: 0.72, MAPE: 0.18). Deployed the model via Flask