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Understanding the problem statement

Initializing necessary libraries and understanding its use

Importing Dataset from amazon AWS and performing basic EDA

Why is it necessary for shuffling data

Performing basic encoding and transformation

Applying a supervised neural network using multi-layer perceptron

Using SGD, LBFGS and ADAM optimizer alternatively

Visualization using strip plot

Applying Logistic Regression

Standard Scaling and normalizing the dataset

Applying Random Forest Regressor as model

Applying Neural Networks using Keras and including Dropouts in between the layer

Understanding "Softmax", "Relu",and "Crossentropyloss"

Applying K-nearest neighbors and visualizing KNN distance and accuracy

Plotting graphs using Scatter Matrix

Applying Recursive Feature Elimination along with SVM

Plotting confusion matrix for visualizing the result

Visualizing the best model using matplotlib

Selecting the best model and making predictions using CSV format

The Human Activity Recognition dataset was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed.Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist.

Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data.

In this data science project with Python, we will complete the analysis of what sorts of people were likely to survive.You will learn to use various machine learning tools to predict which passengers survived the tragedy.

The goal of this machine learning project is to predict which products existing customers will use next month based on their past behaviour and that of similar customers.

In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

Problem Statement Overview

03m

Data Set Overview

02m

Shuffle the Data

01m

Separate Input/Ouput Labels

00m

Encoding Labels

01m

Classification Models

02m

Neural Network Model

09m

Graphical Representation of Data

06m

Logistic Regression

11m

Scalar VS PCA

02m

Transformation Function - TSNE

02m

Activation Functions

02m

Keras Neural Network

01m

Random Forest Classifier

01m

SGD Model

04m

K-Nearest Neighbour

00m

Support Vector Classification

10m

TensorFlow Model

08m

K-Neighbour Classifier

05m

Confusion Matrix

06m

Multiple Classifiers

01m

Grid Search

05m

Conclusion

01m