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EshikaShahEshikaShah
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Merge pull request #7 from Team-Fourth-Dimension/master
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2 parents 280d932 + f44ae7b commit cea696e

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‎EDA.py

+51-4
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@@ -10,7 +10,7 @@
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# To find out the maximum no of customer's belonging to which age group
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def by_age_count(cleaned_profile):
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sns.countplot(x="age_by_decade",data=cleaned_profile)
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sns.countplot(x="age_by_decade",data=cleaned_profile, palette='rocket')
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# Gender Distribution of our customer
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def by_gender_count(cleaned_profile):
@@ -28,14 +28,29 @@ def by_gender_count(cleaned_profile):
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'''
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x = ["F", "M", "O"]
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y = [6129,8484,212]
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plt.bar(x, y)
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plt.bar(x, y, color='c')
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# Gender conts by membership year
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def gender_by_year(cleaned_profile):
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membership_date = cleaned_profile.groupby(['became_member_on', 'gender']).size()
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membership_date = membership_date.reset_index()
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membership_date.columns = ['became_member_on', 'gender', 'count']
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# plot a bar graph for age distribution as a function of gender in membership program
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plt.figure(figsize=(10, 5))
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sns.barplot(x='became_member_on', y='count', hue='gender', data=membership_date)
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plt.xlabel('Membership Start Year')
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plt.ylabel('Count');
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plt.title('Gender counts by membership year')
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# An Overview of what income range facilitates more membership
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def by_income_range(cleaned_profile):
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sns.countplot(x="income_range",data=cleaned_profile)
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def by_member_year(cleaned_profile):
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sns.countplot(x="became_member_on",data=cleaned_profile)
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sns.countplot(x="became_member_on",data=cleaned_profile, palette='Set3')
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# Comparing the Gender-wise distribution of our customer's income
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def income_by_gender(cleaned_profile):
@@ -44,4 +59,36 @@ def income_by_gender(cleaned_profile):
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z = cleaned_profile[cleaned_profile['O']==1]
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sns.kdeplot(x['income'],label='Female')
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sns.kdeplot(y['income'],label='Male')
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sns.kdeplot(z['income'],label='Other')
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sns.kdeplot(z['income'],label='Other')
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# Some data visualization of events related to offers
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# Some numerical data regarding the offer events
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'''
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offers['offer viewed'].value_counts()
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0 98945
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1 49860
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offers['offer received'].value_counts()
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0 82304
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1 66501
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offers['offer completed'].value_counts()
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0 116361
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1 32444'''
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# Representaion of people who viewed and didn't view the offer on recieving the offer
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def offers1():
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x = ["Viewed", "Not viewed"]
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y = [49860,16641]
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plt.pie(y, labels = x,autopct='%1.2f%%', explode=(0.0,0.1), colors=['#ff9999','#66b3ff'])
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def offers2():
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x = ["Completed", "Left viewed"]
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y = [32444,17416]
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plt.pie(y, labels = x,autopct='%1.2f%%', explode=(0.0,0.1), colors = ['#99ff99','#ffcc99'])
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‎Notebook.py

+1-2
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@@ -58,7 +58,6 @@ def cleaning_data():
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# OneHotEncoding the gender column
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ohe = pd.get_dummies(cleaned_profile['gender'])
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cleaned_profile = pd.concat([cleaned_profile,ohe],axis=1)
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cleaned_profile = cleaned_profile.drop(['gender'],axis=1)
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# To convert the became_member_on to date-time stamp because the machine will not
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# understand data corresponding to date in integer form.
@@ -103,7 +102,7 @@ def cleaning_data():
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transactions = transactions.drop(['offer completed','offer viewed','offer received'],axis=1)
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transactions = transactions.drop(['transaction','record'],axis=1)
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transactions = transactions.rename(columns={'record_value':'amount'})
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transactions['amount_range'] = pd.cut(transactions['amount'], bins=range(0,1150,50),right=False, labels=['50','100', '150', '200','250','300','350','400','450','500','550','600','650','700','750','800','850','900','950','1000','1050','1100'])
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# cleaning offers
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offers = offers.drop(['transaction','record'],axis=1)
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offers = offers.rename(columns={'record_value':'offer_id'})

‎README.md

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@@ -15,9 +15,22 @@
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- [x] Data imported.
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- [x] Data cleaning started.
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- [x] Landing page of our website created.
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- 29th August, 2020.
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- 29th August, 2020.
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- [x] Data cleaning completed.
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- [x] Start Exploratory Data Analysis.
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- 30th August, 2020.
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- [x] Exploratory Data Analysis completed.
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- [x] Creation of Input page begun.
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- 31st August, 2020.
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- [x] Tried to merge the data into one pipeline.
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- [x] Eliminated redundant features from the dataset.
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- 1st September, 2020.
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- [x] Merging all the useful features in one dataframe.
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- [x] Algorithm for the model was decided.
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- 2nd September, 2020.
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- [x] Input page creation completed.
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- [x] Added an encoded columns to the merged columns.
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## 📄 Abstract
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The data simulates how people make purchasing decisions and how those decisions are influenced by promotional offers.
@@ -32,7 +45,8 @@ We aim to create a web-app which will be used to predict the best possible offer
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### 📍 Major Checkpoints and Pipelines
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- ⛳ Data Science
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- [ ] Data cleaning and pipelining
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- [x] Data cleaning and pipelining
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- [x] Exploratory Data Analysis
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- [ ] Building a model
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- [ ] Training the model
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- [ ] Testing the model
@@ -44,8 +58,8 @@ We aim to create a web-app which will be used to predict the best possible offer
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- [ ] Testing the API
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- ⛳ Web Development
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- [ ] Front End
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- [ ] Landing page
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- [ ] Input Form
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- [x] Landing page
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- [x] Input Form
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- [ ] Visualization of graphical data
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- [ ] Back End
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- [ ] Integrating the API with the web-app

‎public/css/landing.css

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body .container{
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width: 85%;
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}
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.app-logo{
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margin: 0;
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}
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.app-logo div #logo{
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width: 85px;
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height: 50px;
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margin-top: 10px;
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}
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.app-logo .row .col{
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padding: 0;
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}
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#app-name{
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float: right;
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font-family: Calibri;
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color: #9C9999;
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font-size: 20px;
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}
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#landing-h1{
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font-family: Libre Caslon Display;
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font-style: normal;
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font-weight: normal;
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font-size: 100px;
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line-height: 110px;
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margin-top: 0;
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word-spacing: 6px;
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}
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#landing-captions{
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font-family: calibri;
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font-style: normal;
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font-weight: normal;
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font-size: 25px;
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line-height: 30px;
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color: #727272;
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}
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#offer{
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color: #6775F7;
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}
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#graphics{
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padding-left: 60px;
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padding-top: 25px;
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width: 703px;
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height: 533px;
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}
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.btn
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{
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border-radius: 50px;
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background-color: #6775F7;
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}
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.btn:hover{
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background-color: #293ce9;
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}
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.landing-btn{
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margin-top: 30px;
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}
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‎public/css/landing_alt.css

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/* all */
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* {
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padding: 0;
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margin: 0;
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box-sizing: border-box;
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text-decoration: none;
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}
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/* header */
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header {
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height: 150px;
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width: 100%;
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}
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.head-right {
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padding: 30px;
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float: right;
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display: flex;
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}
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#name {
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padding: 15px 0;
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font-size: 20px;
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font-family: Calibri;
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color: #9C9999;
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}
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#logo {
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height: 60px;
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width: auto;
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}
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/* main */
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.container {
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margin: 0 140px;
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display: flex;
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}
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.container-left {
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float: left;
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margin-right: 80px;
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}
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.container-left h1 {
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font-size: 60px;
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line-height: 80px;
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font-family: Libre Caslon Display;
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padding-bottom: 40px;
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}
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.container-left h1 span {
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color: #6775F7;
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}
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.container-left p {
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font-size: 20px;
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line-height: 30px;
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color: #9C9999;
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padding-bottom: 40px;
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}
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.container-left button {
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padding: 15px 40px;
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border-radius: 50px;
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color: white;
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background-color: #6775F7;
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}
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.container-left button:hover {
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background-color: #2e41f2;
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cursor: pointer;
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transition: 1s ease;
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}
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.container-right {
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float: right;
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}

‎public/images/graphics.png

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‎public/images/logo.jpg

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‎views/index.html

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<!DOCTYPE html>
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<html>
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<head>
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<!--Import Google Icon Font-->
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<link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">
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<link href="https://fonts.googleapis.com/css2?family=Libre+Caslon+Display&display=swap" rel="stylesheet">
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<!--Import materialize.css-->
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<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/1.0.0/css/materialize.min.css">
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<link rel="stylesheet" href="../public/css/landing.css">
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<!--Let browser know website is optimized for mobile-->
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<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
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</head>
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<body>
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<div class="row app-logo">
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<div class="col s2 offset-s10">
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<div class="row">
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<div class="col s8"><p id="app-name">Growth Square</p></div>
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<div class="col s4"><img id="logo" src="../public/images/logo.jpg" alt="logo"></div>
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</div>
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</div>
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</div>
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<div class="row body-content container">
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<div class="col m6 s12">
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<h1 id="landing-h1">Run <span id="offer">offers</span> which actually works.</h1>
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<p id="landing-captions">Provide us with your customer details, and we help you with the best types of offers that can drive maximum growth in your business.</p>
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<a class="btn waves-effect waves-light btn-large landing-btn">Get started</a>
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</div>
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<div class="col m6">
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<img src="../public/images/graphics.png" id="graphics" alt="">
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</div>
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</div>
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<script src="https://code.jquery.com/jquery-3.3.1.min.js"></script> <!-- jquery should come before materialize.js -->
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<script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/1.0.0/js/materialize.min.js"></script>
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<script type="text/javascript" src="js/materialize.min.js"></script>
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</body>
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</html>

‎views/index_alt.html

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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<link rel="stylesheet" href="../public/css/landing_alt.css">
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<!-- Google Fonts -->
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<link href="https://fonts.googleapis.com/css2?family=Libre+Caslon+Display&display=swap" rel="stylesheet">
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</head>
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<body>
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<!-- Logo -->
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<header>
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<div class="head-right">
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<p id="name">Growth Square</p>
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<img id="logo" src="../public/images/logo.jpg">
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</div>
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</header>
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<!-- Main -->
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<div class="container">
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<div class="container-left">
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<h1>Practical <span>Plans</span> <br> for <br> Practical Businesses.</h1>
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<p>We provide valuable information to companies to allow them to grow at a booming rate by analysing market trends. Input the details of your customers and get relevant offer recommendations that can exponentially grow your business.</p>
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<button>Get Started</button>
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</div>
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<div class="container-right">
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<img id="graphics" src="../public/images/graphics.png" >
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</div>
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</div>
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</body>
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</html>

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