Car Price Prediction
The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset
Multiple-Linear-Regression-1. Consider only the below columns and prepare a prediction model for predicting Price of Toyota Corolla.
Assignment-05-Multiple-Linear-Regression-2. Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few y…
Iris-Dataset-Prediction-using-Unsupervised-ML This project involves the analysis of the Iris dataset using Python. It includes code to determine the optimum number of clusters using the K-means clustering algorithm, visualizations such as scatter plot, pair plots and hist plots, and other insights into the dataset.
A simple analysis of Zlatan Ibrahimović's club career.
Seaborn Visualization on Titanic Dataset Visual exploration of different features on No. of people survived or otherwise Visualization using FacetGrid function, Lambda function and criterion function Visualization of subplots
Visualization using Matplotlib and Seaborn
Strip Plot, Grouping with Strip Plot, Swarm Plot, Box and Violin Plot, placing plots together, Combining the plots, Joint Plot, Density Plot, Pair Plot
Python matplotlib, seaborn and plotly plots cheat sheet
Imputation and Visualization of a dirty bank data with the help of NumPy, Pandas, Matplotlip and Seaborn libraries.
This repository contains the file for the task that was done as part of my internship in The Sparks Foundation with specialization - Data Science & Business Analytics.
Multiple-Linear-Regression-1. Consider only the below columns and prepare a prediction model for predicting Price of Toyota Corolla.p
Higher Diploma in Science in Computing (Data Analytics) - Programme Module: Programming and Scripting (COMP08049)
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describin…
Used libraries and functions as follows:
Performing the K-Nearest-Neighbor Algorithm.
Add a description, image, and links to the pairplot topic page so that developers can more easily learn about it.
To associate your repository with the pairplot topic, visit your repo's landing page and select "manage topics."