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Machine_Learning_Python

Applied Machine learning with python libraries. Trained model by splitting dataset in to train and test sets(80-20). Supervised machine learning is used to solve both classification and regression problems.

Linear Regression

  • A Linear Model that Predicts Salary Based on Years of experience.
  • The model can be visualized from output.png file.
  • Libraries used : Pandas | Matplotlib | Sklearn

Multiple Linear Regression

  • A Linear Model that Predicts profit Based on various expenditures done by company.
  • The Difference of acutal and predicted values can be visualized from output.png file.
  • Libraries used : Pandas | Matplotlib | Sklearn

Polynomial Regression

  • A Linear Model that Predicts Salary Based on your level post at company.
  • Used Feature Scaling.
  • The Difference of Linear and Polynomial model can be visualized from output.png file.
  • Libraries used : Pandas | Matplotlib | Sklearn

Logistic Regression

  • A Model that Predicts whether the product will get purchased or not based on age and salary of person.
  • The model can be visualized from output.png file.
  • Comparing result through Confusion Matrix.
  • Libraries used : Pandas | Matplotlib | Sklearn | Numpy

k-Nearest Neighbors(KNN) Classifier

  • A Model that Predicts whether the product will get purchased or not based on age and salary of person.
  • The model can be visualized from output.png file.
  • Used Feature Scaling.
  • Comparing result through Confusion Matrix.
  • Libraries used : Pandas | Matplotlib | Sklearn | Numpy

Random Forest

  • A classification algorithm consisting of many decisions trees that predicts type of flower.
  • This model gives highest accuracy than others.
  • Libraries used : Pandas | Matplotlib | Sklearn

Natural Language Processing(NLP)

  • A Model that Predictes whether reviews are good or bad.
  • Algorithmn used to train model - GaussianNB
  • Libraries used : Pandas | Matplotlib | Sklearn | re | nltk

Sentiment Analysis

  • It analyse the statement based on ratio of postive, negative and neutrality.
  • Libraries used : vaderSentiment