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fatihhsahin1 / repository
This data analysis project showcases my ability to perform customer segmentation using Python and Jupyter Notebook. Based on a tutorial from "Absent Data" YouTube channel, I used KMeans clustering algorithm to identify the best marketing group for a mall's customers.
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This data analysis project showcases my ability to perform customer segmentation using Python and Jupyter Notebook. Based on a tutorial from "Absent Data" YouTube channel, I used KMeans clustering algorithm to identify the best marketing group for a mall's customers.
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Olow304 /
The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run this collections either in Jupyter notebook or python alone.
66/100 healthmwermelinger /
Jupyter notebooks and datasets for this course
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Project using machine learning to predict depression using health care data from the CDC NHANES website. A companion dashboard for users to explore the data in this project was created using Streamlit. Written with python using jupyter notebook for the main project flow/analysis and visual studio code for writing custom functions and creating the dashboard.
dshahid380 /
Welcome to data analysis with pandas tutorial. In this tutorial i have covered all the topic of pandas and tried to explain with lesser number of words.This tutorial is totally written in jupyter notebook so that anyone can clone and run it.
55/100 healthanubhavanand12qw /
The coding has been done on Python 3.65 using Jupyter Notebook. This program fetches LIVE data from TWITTER using Tweepy. Then we clean our data or tweets ( like removing special characters ). After that we perform sentiment analysis on the twitter data and plot it for better visualization. The we fetch the STOCK PRICE from yahoo.finance and add it to the data-set to perform prediction. We apply many machine learning algorithms like (random forest, MLPClassifier, logistic regression) and train our data-set. Then we perform prediction on untrained data and plot it with the real data and see the accuracy.
62/100 healthrajchandran006-ops /
RFD Classification Machine Learning project developed using Python and Jupyter Notebook. This project includes data preprocessing, exploratory data analysis, feature engineering, and implementation of multiple classification algorithms such as Logistic Regression, Random Forest, SVM, KNN, and Naive Bayes for prediction and accuracy evaluation.
75/100 health