Machine Learning
1.Introducing Scikit-Learn
Scikit-Learn is one of the most-used open-source machine learning library for Python. Scikit-Learn provides various unsupervised and supervised learni
2.Dissecting the Practice Dataset
Analyze how the default practice datasets are organized using keys. KeysKeys are normally composed of data, target, target_name, feature_names & DESCR
3.Importance of Splitting Train & Test Set
On previous post, I had emphasized the importance of splitting data into train and test data-sets. On this post, let us see what happens to the estima
4.Cross Validation in Scikit-Learn
a process of resampling machine learning model in a limited data sampleFrom the previous post, we learned the importance of splitting data-set into tr
5.Data Preprocessing in Scikit-Learn
Until now, we have learned about various machine learning techniques to enhance the performance of the model. However, handling and managing the data
6.Feature Scaling in Scikit Learn
Data Preprocessing is not just about encoding the data and converting the data type within the dataset. It also requires arduous steps to adjust the w
7.Using Scikit-Learn to Predict Titanic Survivors
Kaggle : Titanic DatasetAs a review, we will be using train.csv from Kaggle's Titanic dataset to predict the survivors from the disaster.Input OutputP
8.Evaluation Metrics in Machine Learning - Accuracy
So far, we have studied various techniques (ex- train/test-split, GridSearchCV, Standardization, Normalization, Data Preprocessing) to enhance our mac
9.Evaluation Metrics in Machine Learning - Confusion Matrix
Confusion Matrix which is often used as an evaluation metrics in binary classification shows how much the model is confused while performing the predi
10.Evaluation Metrics in Machine Learning - Precision / Recall
Precision and Recall are evaluation metrics which emphasize the performance in positive data-set. Precision = (TP) / (TP + FP) ratio of correctly pr
11.Evaluation Metrics in Machine Learning - F1 Score
F1-score is the weighted average of precision and recall. Hence, since it takes both FP and FN into account, better the balance between precision and
12.Evaluation Metrics in Machine Learning - ROC / AUC
Receiver Operation Curve (ROC) and Area Under Curve (AUC) are important evaluation metrics used in evaluating the performance of the binary classifica
13.Classification in Machine Learning - Decision Tree
Brief Summay A supervised learning is a machine learning task of learning a function that maps an input to an output based on example input-output p
14.Decision Tree to Classify Human Activity
Today, we will be using decision tree to perform classification prediction on UCI Machine Learning Repository's Human Activity Recognition dataset. Th
15.Ensemble Learning : Voting and Bagging
* Ensemble Learning * : process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a partic