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Understanding the problem statement

Importing the problem statement

Installing Keras and LSTM

Importing the necessary libraries for applying Neural Networks

Performing basic EDA and checking for the null values

Imputing the null values using appropriate method

Plotting a Time Series plot

Creating a Dataset matrix for applying LSTM

Sequentially initializing a Neural Networks

Defining the error function

Understanding solver used "Adam"

Applying LSTM as training model

Visualizing the loss and accuracy with each epoch

Tuning the final model and using it to make predictions

Saving the predictions made in CSV format

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In this project, we are going to talk about insurance forecast by using regression techniques.

Deep Learning Architectures

06m

DNN - Deep Neural Network

00m

CNN - Convolutional Neural Network

01m

RNN - Recurrent Neural Network

02m

Deep Belief & Boltzman Network

01m

Deep Neural Network - Graphical Representation

20m

Activation Functions

02m

Perceptron and Bias

02m

Convolutional Neural Network - Graphical Representation

08m

Recurrent Neural Network - Graphical Representation

07m

Deep Belief & Boltzman Network - Graphical Representation

00m

Problem Statement

01m

Data Set

05m

Setting up Libraries

15m

Setting Theano as backend

01m

Import Libraries

02m

Create Seed function

01m

Normalize the dataset

04m

Split dataset into training and testing

06m

Create Dataset Matrix

07m

Reshape Dataset

05m

Create RNN or LSTM Model

04m

Make Predictions

08m

Calculate Mean Squared Error

03m

Conclusion

05m