https://github.com/haripasapuleti/oibsip
This Oasis Infobyte internship involves completing data science tasks like classification, prediction, and spam detection using Python. Projects include iris flower classification, car price prediction, email spam detection, and sales forecasting.
https://github.com/haripasapuleti/oibsip
data-science data-visualization linear-regression random-forest-classifier sklearn
Last synced: about 1 month ago
JSON representation
This Oasis Infobyte internship involves completing data science tasks like classification, prediction, and spam detection using Python. Projects include iris flower classification, car price prediction, email spam detection, and sales forecasting.
- Host: GitHub
- URL: https://github.com/haripasapuleti/oibsip
- Owner: HariPasapuleti
- Created: 2023-10-04T17:19:39.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-23T16:59:32.000Z (over 1 year ago)
- Last Synced: 2025-01-23T17:27:16.206Z (over 1 year ago)
- Topics: data-science, data-visualization, linear-regression, random-forest-classifier, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 1.44 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# OIBSIP - Oasis Infobyte Intern Program
OASIS INFOBYTE offers a variety of Data Science,Machine learning and AI services. Got the chance for 1 month intern of Data Science.
There are 5 task given to complete. After completing 3 tasks, if everything looks fine, the intern will be certified. Glad to be a part of OASIS INFOBYTE intern program.
## Task1 - Iris Flower classification
The aim of the iris flower classification is to predict flowers based on their specific features by help of machine learning models.
• The project code completely done using Python
• This project dataset contains iris dataset iris.csv link :
https://www.kaggle.com/datasets/saurabh00007/iriscsv
• Required packages are for this project is pandas, numpy, matplotlib, seaborn, pickle, sklearn, Logistic Regression, Decision Tree, K-Neighbors, Naïve Bayes, SVC.
• Model trained and tested with supportive models like Decision Tree, K-Neighbors, Naïve Bayes, SVC
## Task2 - Car Price Prediction
To be able to predict used cars market value can help both buyers and sellers. There are lots of individuals who are interested in the used car market at some points in their life because they wanted to sell their car or buy a used car. In this process, it’s a big corner to pay too much or sell less then it’s market value.
• The project code completely done using Python
• Required packages installed, that are pandas, sklearn, keras_tuner, seaborn, tensorflow, tabulate, matplotlib, LinearRegression, RandomForestClassifier, metrics.
• This project dataset contains CarPrice dataset CarPrice.csv link :
https://github.com/amankharwal/Website-data/blob/master/CarPrice.csv
## Task3 - Email Spam Detection
One of the primary methods for spam mail detection is email filtering. It involves categorize incoming emails into spam and non-spam. Machine learning algorithms can be trained to filter out spam mails based on their content and metadata.
• The project code completely done using Python
• Required packages installed, that are pandas, re, nltk, sklearn, seaborn, matplotlib, missingno, wordcloud, collections,Logistic Regression, Decision Tree,RandomForestClassifier, K-Neighbors, Naïve Bayes, SVC.
• This project dataset contains spam dataset spam.csv link :
https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
## Task4 - Sales Prediction
Predicting the sales of a store
• The project code completely done using Python
• Required packages installed, that are pandas, numpy, seaborn, matplotlib, sklearn, LinearRegression.
• This project dataset contains Sales dataset Advertising.csv link :
https://www.kaggle.com/datasets/bumba5341/advertisingcsv