Higher Diploma in Science in Computing (Data Analytics) - Programme Module: Programming and Scripting (COMP08049)
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Updated
Apr 30, 2020 - Python
Higher Diploma in Science in Computing (Data Analytics) - Programme Module: Programming and Scripting (COMP08049)
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
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
Python- Data Visualization
Used libraries and functions as follows:
Performing the K-Nearest-Neighbor Algorithm.
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…
Visualization using Matplotlib and Seaborn
This project analyses different clustering methods over three different datasets
Given a person's data, the task is to predict that in which category the person's weight should fit in. This is a Multiclassification project.
Used libraries and functions as follows:
Exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. In this exercise, iris data was visualized using box plots, pairplot, subplot, and scatter plots for better comprehension of the dataset.
The dataset used for this project is taken from the official UCI Machine Learning Repository.
1st Project for the Post Graduate Programme in Data Science and Business Analytics at the University of Texas at Austin - Exploratory Data Analysis
finding correlation between store features and sales
In the particular notebook i have made some of the widely useful charts and graphs in the industry to visualize the data with the meaningful insights which will help stakeholders to get an better understanding of the current scenario of the company's infrastructure.
Task-3 Completed as a DSBA Intern @ The Sparks Foundation
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