Objective $$ Do the necessary data exploration, data pre$-$processing, data preparation and build$$ models using classification approaches to predict whether the user clicks the links for online$$ advertisements$$

Dataset: onlineuseradvertisement dataset

$1.$ Import Libraries$/$Dataset$$

$1.$ Download the dataset$$

$2.$ Import the required libraries$$

$2.$ Data Visualization and Exploration $[1$ M$]$

a$.$ Print at least $5$ rows for sanity check to identify all the features present in the dataset$$ and if the target matches with them. $(0\mathrm{.}5$ M$)$

b$.$ Print the description and shape of the dataset. $(0\mathrm{.}5$ M$)$

c$.$ Provide appropriate visualization to get an insight about the dataset. $(0\mathrm{.}5$ M$)$

d$.$ Try exploring the data and see what insights can be drawn from the dataset. $(0\mathrm{.}5$ M$)$

$3.$ Data Pre$-$processing and cleaning $[3$ M$]$

a$.$ Do the appropriate preprocessing of the data like identifying NULL or Missing Values if$$ any, handling of outliers if present in the dataset, skewed data etc. Apply appropriate$$ feature engineering techniques for them. $(1$ M$)$

b$.$ Apply the feature transformation techniques like Standardization, Normalization, etc.$$ You are free to apply the appropriate transformations depending upon the structure$$ and the complexity of your dataset. $(1$ M$)$

c$.$ Do the correlational analysis on the dataset. Provide a visualization for the same. $(1$ M$)$

$4.$ Data Preparation $[1$ M$]$

a$.$ Do the final feature selection and extract them into Column X and the class label into$$ Column into Y$.(1$ M$)$

b$.$ Split the dataset into training and test sets.$$

$5.$ Model Building $[3$ M$]$

a$.$ Perform Model Development using Logistic regression and Decision tree. Deep$$ Learning Models are strictly not allowed. $(2$ M $)$

b$.$ Train the model and print the training accuracy and loss values. $(1$ M$)$

$6.$ Performance Evaluation $[2$ M$]$

a$.$ Print the confusion matrix. Provide appropriate analysis for the same. $(0\mathrm{.}5$ M$)$

b$.$ Do the prediction for the test data and display the results for the inference. $(0\mathrm{.}5$ M$)$

Sample Data set:

Age Gender Income Location Device Interest$\_$Category Time$\_$Spent$\_$on$\_$Site Number$\_$of$\_$Pages$\_$Viewed Click

$056$ Male $99003$ Rural Mobile Sports $81\mathrm{.}9793237270$

$146$ Male $72395$ Suburban Tablet Sports $59\mathrm{.}8540702731$

$232$ Male $59758$ Suburban Tablet Sports $78\mathrm{.}861988820$

$360$ Male $74312$ Urban Tablet Technology $9\mathrm{.}41157880160$

$425$ Female $88670$ Suburban Mobile Fashion $76\mathrm{.}4684086190$

$538$ Female $35434$ Rural Tablet Technology $94\mathrm{.}45593614101$

$656$ Male $84047$ Urban Mobile Sports $62\mathrm{.}5412114441$

$736$ Male $67775$ Rural Mobile Travel $46\mathrm{.}1140327731$

$840$ Male $95769$ Rural Tablet Technology $36\mathrm{.}904074591$

$928$ Male $86677$ Rural Desktop Fashion $78\mathrm{.}92223577171$

$1028$ Male $65734$ Suburban Desktop Sports $84\mathrm{.}67213784140$

$1141$ Female $60603$ Suburban Tablet Technology $105\mathrm{.}161274101$

$1253$ Female $96708$ Rural Tablet Travel $44\mathrm{.}36841948130$

$1357$ Female $88803$ Rural Mobile Technology $93\mathrm{.}52381535120$

$1441$ Female $59332$ Urban Tablet Fashion $69\mathrm{.}5559197810$

$1520$ Male $41312$ Urban Mobile Technology $106\mathrm{.}5261964191$

$1639$ Male $64179$ Suburban Tablet Travel $65\mathrm{.}69839245191$

$1719$ Male $91442$ Suburban Desktop Travel $64\mathrm{.}5274302761$

$1841$ Male $60379$ Urban Desktop Fashion $71\mathrm{.}45586437131$

$1961$ Male $37297$ Rural Tablet Travel $91\mathrm{.}17126584191$

$2047$ Female $22011$ Suburban Mobile Sports $115\mathrm{.}9394051131$

$2155$ Male $64014$ Rural Desktop Travel $13\mathrm{.}4597857341$

$2219$ Female $31738$ Urban Mobile Sports $11\mathrm{.}1449792721$

$2338$ Female $67498$ Suburban Tablet Technology $112\mathrm{.}8182888150$

$2450$ Female $31120$ Suburban Tablet Sports $65\mathrm{.}93569693121$

$2529$ Female $28767$ Urban Desktop Sports $75\mathrm{.}7138466870$

$2639$ Male $42852$ Suburban Tablet Technology $60\mathrm{.}47871418180$

$2761$ Female $99239$ Urban Desktop Travel $15\mathrm{.}77060705130$

$2842$ Female $20929$ Suburban Mobile Sports $93\mathrm{.}33027852170$

$2944$ Female $97487$ Urban Desktop Travel $90\mathrm{.}9308341941$

$3059$ Male $37438$ Suburban Desktop Technology $40\mathrm{.}84588197141$

$3145$ Male $68515$ Rural Mobile Fashion $40\mathrm{.}2175132130$

$3233$ Male $25744$ Rural Desktop Travel $101\mathrm{.}351758830$

$3332$ Female $62417$ Rural Desktop Travel $13\mathrm{.}32108464180$

$3464$ Female $27069$ Suburban Tablet Fashion $106\mathrm{.}3270921141$

$3561$ Male $62359$ Suburban Mobile Fashion $34\mathrm{.}51609434150$

$3620$ Female $39799$ Rural Desktop Technology $73\mathrm{.}1932459930$

$3754$ Male $90188$ Rural Mobile Travel $112\mathrm{.}639330611$

$3824$ Male $27597$ Urban Mobile Travel $82\mathrm{.}98408768170$

$3938$ Female $67093$ Urban Desktop Technology $52\mathrm{.}6594284331$

$4026$ Female $57226$ Urban Tablet Travel $84\mathrm{.}29643402141$

$4156$ Female $92127$ Rural Mobile Sports $44\mathrm{.}5576581830$

$4235$ Female $97362$ Rural Desktop Sports $36\mathrm{.}88471788130$

$4321$ Male $46417$ Rural Mobile Sports $119\mathrm{.}618577150$

$4442$ Male $38693$ Urban Tablet Travel $91\mathrm{.}34087659181$

$4531$ Male $99674$ Suburban Tablet Sports $79\mathrm{.}1427974141$

$4626$ Male $73161$ Rural Tablet Sports