mob-classificaton-and-prediction - Kaparapu Abhichand ce17b104

Table of Contents:
- Data Summary
- Data Cleaning
- Exploratory Data Analysis
- Importing libraries
- Upsampling, Splitting the Dataset & Standardization
- Model Building on Main dataset
  Logistic Regression Model
  K-Nearest Neighbours Model
  SVC
  Decision Trees Classifier
  Random Forest Classifier
  XGBoost Classifier
- Evolution matrix
- Scaling
  Logistic Regression Model
  K-Nearest Neighbours Model
  SVC
  logistic regression after polynomial degree 2
- HyperParameter Tuning
  Logistic Regression Model
  Logistic regression after polynomial degree 2
  K-Nearest Neighbours Model
  XGBoost Classifier
- Comparing base fit and tuned fit,scaled tuned fit and scaled fit.
- Final Conlusion

!pip install jovian --upgrade -q
import jovian
jovian.set_project('ml_projectzzz')

!pip install jovian opendatasets --upgrade --quiet

dataset_url = 'https://www.kaggle.com/iabhishekofficial/mobile-price-classification?select=train.csv'

import opendatasets as od
od.download(dataset_url)

Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds
Your Kaggle username: abhichand
Your Kaggle Key: ··········

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Downloading mobile-price-classification.zip to ./mobile-price-classification

Kaparapu Abhichand ce17b1042 years ago

Mob Classificaton And Prediction