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hello-ml's Introduction

Hello World in Machine Learning 👋

Tools & Technology :

This repo is all about how to get started with Machine Learning and all the tricks required to perform necessary task and how to make a model.

Here we have taken the famous Iris Dataset as our running example. This is a very simple and easy dataset to get started with ML.

Stepping into the world of ML in the right way ✔️

Libraries Used :

Numpy Pandas Matplotlib seaborn XGBoost Scikit-Learn

Setting up environment :

  1. Download and install anaconda installer.

  2. Then go to the python shell or terminal and create a new environment e.g. Hello_ML .Tutorial for creating new environment is here.

  3. Write this commands ony by one to install the required libraries in the shell.

      $pip install numpy
  $pip install pandas
  $pip install matplotlib
  $pip install seaborn
  $pip install xgboost
  $pip install scikit-learn

Given Approaches :

  1. Basic Visualization like scatterplots and barcharts are given here to understand the data variance.
  2. Visualizing the pairplots also helping to understand feature to fature correltion.
  3. We have also tried to add the probabbility density function (pdf) and cumulative density function(cdf) to understand the hidden patterns also.
  4. We also have used different scaling process to retrieve the best trainable data.
  5. Outlier detection also helped to find the actual data.
  6. Now it comes to choose the best model to fit to the trainable data. We have taken probably all type of models to find better accuracy.

Task 1:

To perform the following tasks:

  1. To make PDFs and CDFs of Iris flower based on their species.
  2. To make the Violinplots of Iris flower based on their species.
  3. To make the boxplots of Iris flower based on their species.
  4. To quantify the resulting plots on own words.

hello-ml's People

Contributors

ankit152 avatar dibyanshushekhardey avatar dikshanasa avatar mangalam0512 avatar sagnik1511 avatar

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Forkers

codassassin sorba160 mangalam0512 vaibhavjalani sagnik1511 dibyanshushekhardey dikshanasa

hello-ml's Issues

Perform K-Nearest Neighbors

  1. Given the iris.csv file, apply k-nearest neighbors on the data to classify species with a test size of 20% with k=4.
  2. Visualise the confusion matrix.
  3. Print the classification report.

Perform AdaBoost Classifier

  1. Given the iris.csv file, apply AdaBoost on the data to classify species with a test size of 20%.
  2. Visualise the confusion matrix.
  3. Print the classification report.

Perform SGDClassifier

  1. Given the iris.csv file, apply SGDClassifier with different losses on the data to classify species with a test size of 20% .
  2. Visualise the confusion matrix.
  3. Print the classification report.

Perform Random Forest Classifier

  1. Given the iris.csv file, apply Random Forest Classifier on the data to classify species with a test size of 20%.
  2. Visualise the confusion matrix.
  3. Print the classification report.

Perform Decision Trees

  1. Given the iris.csv file, apply decision trees on the data to classify species with a test size of 20% with depth =6.
  2. Visualise the confusion matrix.
  3. Print the classification report.

Update Readme

Update the readme of landing page of this repository Hello ML.

Perform Logistic Regression

  1. Given the iris.csv file, apply logistic regression on the data to classify species with a test size of 20%.
  2. Visualise the confusion matrix.
  3. Print the classification report.

Perform XGBoost

  1. Given the iris.csv file, apply XGBoost on the data to classify species with a test size of 20%.
  2. Visualise the confusion matrix.
  3. Print the classification report.

Perform Extra Trees Classifer

  1. Given the iris.csv file, apply extra trees classifier on the data to classify species with a test size of 20%.
  2. Visualise the confusion matrix.
  3. Print the classification report.

Perform Naive Bayes

  1. Given the iris.csv file, apply Naive Bayes on the data to classify species with a test size of 20% .
  2. Visualise the confusion matrix.
  3. Print the classification report.

Perform SVM

  1. Given the iris.csv file, apply SVM on the data to classify species with a test size of 20% .
  2. Visualise the confusion matrix.
  3. Print the classification report.

Perform Gradient Boosting Classifier

  1. Given the iris.csv file, apply Gradient Boosting Classifier on the data to classify species with a test size of 20%.
  2. Visualise the confusion matrix.
  3. Print the classification report.

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