naruarjun

naruarjun

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK1\n",
    "So this is a classification model for Telco-Customer-Churn. So basically we have to predict whether churn is yes or no. The model is a simple linear SVM and achieved a accuracy of 75% on the test set."
   ]
  },

naruarjun

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK1\n",
    "So this is a classification model for Telco-Customer-Churn. So basically we have to predict whether churn is yes or no. The model is a simple linear SVM and achieved a accuracy of 75% on the test set."
   ]
  },

naruarjun

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK1\n",
    "So this is a classification model for Telco-Customer-Churn. So basically we have to predict whether churn is yes or no. The model is a simple linear SVM and achieved a accuracy of 100% on the test set."
   ]
  },

naruarjun

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pretrained Classification Model\n",
    "This is a pretrained classification model to classify the natural images dataset. The pretrained network used is InceptionV3 and the deep learning framework used is keras. This model achieved a validation accuracy of about 98.32%. The Model is basically the InceptionV3 architecture with two dense layers on top of it. The output of the following cell contains the entire architecture of the network summarised. I manually divided the data into two folders. Natural_images contained my training data and natural_images_validation contains my validation data"
   ]
  },

naruarjun