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##### [Project Github repository](https://github.com/meysamraz/Google-Maps-Amsterdam-restaurants-analysis)

In this project, I scraped details of Amsterdam restaurants from Google Maps using Selenium and analyzed the data.

Who is this analysis for ?

*   Tourists, those who travel to Amsterdam and those who live in Amsterdam can use this analysis to find the best restaurants.
*   Those who are looking to start a business in the restaurant industry and provide services in Amsterdam

I used Google Maps to get data about restaurants in Amsterdam. Unfortunately, it is not possible for those who live in Iran to access Google APIs :( But this is not the end of the work, I was able to create two robots using Selenium and scrape the Google maps data. The first bot [Google_maps_scraper.py](https://github.com/meysamraz/Google-Maps-Amsterdam-restaurants-analysis/blob/master/Google_maps_scraper.py) is from the Google Map site for scraping. It uses and is able to scrape restaurant reviews and the second bot [Google_scraper.py](https://github.com/meysamraz/Google-Maps-Amsterdam-restaurants-analysis/blob/master/Google_scraper.py) uses Google to collect data.

# Project Overview :

![](/images/demo_scraper1.gif)![](/images/demo_scraper2.gif)![](/images/plot3-6cf1add7.png)![](/images/plot4-74c0a95e.png)![](/images/plot5-bcbb0dc0.png)

*   ### 1 - Scraping data from Google maps
    *   title : name of restaurant
    *   rating : google maps rating (star 0-5) of restaurant
    *   reviews : number of reviews of the restaurant
    *   expense : price range of the restaurant ($ - econony, $$ - regular, $$$  - expensive, $$$ - luxury)
    *   category : what type of food does restaurant serve (Italian - french or etc.)
    *   description : restaurant description in Google maps
    *   services : What kind of services does the restaurant offer? (die-in,takeaway...)
    *   address : The registered address of the restaurant in Google Map

*   ### 2 - Cleaning the data
    *   Convert ratings to number
    *   Remove K and convert number of reveiws to int
    *   Remove "Service options:" from services

*   ### 3 - Analyzing Data
    *   The Number of scrapped restaurants
    *   Min - Meam - Max of Restaurats Rating
    *   Rating vs Reviews
    *   Rating Distribution
    *   Removing outliers and perform analysis
    *   Min - Meam - Max of Restaurats Number of Reviews
    *   How many restaurants have takeaway?
    *   How many restaurants have dine-in?
    *   How many restaurants have both dine-in and takeaway?
    *   Restaurant with most reviews
    *   Restaurant with lowest rating
    *   Restaurant with highest rating and No. reviews
    *   What categories most restaurants are from?
    *   Number of closed Restaurants
    *   In what category are the restaurants in terms of price?
    *   Restaurant with highest rate in each category
    *   Reviews Word Cloud

*   ### 4 - Get latitude and longitude based on the addresses
    *   Which area of Amsterdam has the most restaurants?
    *   Where are most luxury restaurants located ?

## Libraries used in the project

*   [pandas](https://pandas.pydata.org/)
*   [numpy](https://numpy.org/)
*   [matplotlib](https://matplotlib.org/)
*   [seaborn](https://seaborn.pydata.org/)
*   [termcolor](https://pypi.org/project/termcolor/)
*   [wordcloud](https://pypi.org/project/wordcloud/)
*   [geopy](https://geopy.readthedocs.io/)
*   [folium](https://python-visualization.github.io/folium/)



Scraping & EDA Amsterdam Restaurants and Cafés on Google Maps

In this project, I tried to create a model that can predict the price of a laptop based on the criteria of the desired laptop by using the data of the available laptops that I collected from the Digikala website, the largest e-commerce site in Iran.

[Project Github Repository](https://github.com/meysamraz/laptop-price-prediction-end-to-end-project-using-ecommerce-website-data)

[Project Demo](https://loptop-price-prediction.herokuapp.com/)

***Note: Since the price of laptops in Iran is constantly changing, this model is trained based on the data of laptops available on 2022-11-06 (1401-8-15) in Digikala. And in the coming years, it may not have an accurate prediction, and the solution to this problem is to update the model with the most up-to-date data.***

# Project Overview :

## 1 - Collect Data

To collect my data, I used Digikala's secret api. I was able to collect the data I wanted (available laptops with prices) with a simple fitler.

*   #### Collect Laptop main data
    *   id
    *   title\_fa
    *   title\_en
    *   price
    *   image\_url
    *   brand

*   #### Collect Laptop details data
    *   cpu manufacturer
    *   cpu series
    *   cpu model
    *   ram
    *   ram type
    *   internal storage
    *   internal storage type
    *   gpu manufacturer
    *   gpu model
    *   screen resolution
    *   ports

*   #### Merge Collected data

*   #### Remove duplicated rows

*   #### Save data into csv file

## 2 - Take a look at data :

After collecting the data, I started checking the collected data to make sure it was collected correctly

*   #### Check shape of data
*   #### Check is there any null value
*   #### Check data types
*   #### Check number of unique values in each column

## 3 - Cleaning data

Like all machine learning projects, the data doesn't arrive perfect and ready for prediction. At this point, I started cleaning the collected data.

*   #### Convert brands name from persian to english
*   #### Convert ram from persian to english digits
*   #### Clean and convert internal storage to english
*   #### Convert and clean internal storage to english
*   #### Convert and clean laptops screen size
*   #### Clean laptops resolution

## 4 - EDA

![](/images/plot1.png)

For the next step, which is **Feature engineering**, it was necessary to get information about the data. In this step, I analyzed and explored the data.

*   #### Laptops price distribution
*   #### Number of laptops of each brand
*   #### Number of cpu of each cpu manufacturer
*   #### Number of laptops for each ram group
*   #### Number of laptops for each ram type group
*   #### Number of laptops with diffrent internal storage
*   #### Number of laptops for each internal storage group
*   #### Number of laptops with diffrent screen sizes
*   #### Number of laptop with diffrent screen resolution

## 5 - Feature Engineering

In this step, I prepared the features for training the model

*   #### Remove outliers base on laptops price using z score
*   #### Convert screen resolution to number
*   #### Extract Gaming brands from title (asus rog , acer nitro ...)
*   #### Remove brand with only one laptop
*   #### Extract clean gpu model from gpu model column
*   #### Remove laptops with only less than 3 model gpu
*   #### Label endcoding cleand gpu models
*   #### Convert internal storage from tb and gb to mg
*   #### Label encoding internal storage type
*   #### Convert ram from str to int
*   #### Extract port count
*   #### Label encoding ram type
*   #### Label encoding cpu series
*   #### One hot encoding brand - cpu manufacturer - gpu manufacturer (nominal categorical variables)

## 6 - Feature Selection

In this step, I chose the features needed to train the model

*   #### Check correlation
*   #### Mutual information regression

## 7 - Model training

![](/images/plot4.png)

## 8 - Hyperparameter tuning

In this step, I hyperparameter tuned the model that had the best r2 score

## 9 - Cross validation

![](/images/plot5.png)

## 10 - Save model

I pickeld model for use in the gui environment

## 11 - Website

To create website, I used streamlit formwork, a powerful formwork that allows me to create the desired user interface completely using Python.

## 12 - Deploy

I used [Heroku](https://www.heroku.com/) a cloud platform as a service which provide a free hosting to deploy my app on it. it's and amzaing platform gave me so much flexbilte to deploy your apps

## Libraries and FrameWorks used in the project

*   [streamlit](https://streamlit.io/)
*   [pandas](https://pandas.pydata.org/)
*   [numpy](https://numpy.org/)
*   [matplotlib](https://matplotlib.org/)
*   [seaborn](https://seaborn.pydata.org/)
*   [termcolor](https://pypi.org/project/termcolor/)
*   [scikit-learn](https://scikit-learn.org/)
*   [scipy](https://scipy.org/)
*   [xgboost](https://xgboost.readthedocs.io/)



Predicting Laptop Prices: An End-to-End Project Using E-commerce Website Data

[Project Github repository
](https://github.com/meysamraz/Snapp-ds-dashboard)

Snapp! is an Iranian service provider, allowing users to book a car and driver to transport them in a way similar to a taxi. Snapp is currently the most popular online taxi rental service in Iran, so I decided to analyze my use of this popular service.

### Project Overview :





#### 1 - Collecting Data :

Since Snapp did not have an api or source to receive data related to my travel history. I used a bot to collect my data from the snapp web app

#### What data did I collect ?

*   Number of travels
*   Total Hours of travels
*   Total kilometers traveled
*   Travel day title
*   Car that i traveld with
*   Travel cost
*   Travel date
*   trave time (hour)
*   Initial point of travel
*   Travel destination
*   How many star were my travel

#### 2 - Preprocess and Feature engineering collected data :

*   Drop canceld travels
*   Extract destination location from data
*   Extract origin city (translated in english)
*   Extract date and convert it form jalali (persain date unit into gregorian)
*   Translate car model in English
*   Convert travel cost from rial to toman and translate it (English)
*   Split Curreny from price
*   Drop uneeded columns
*   Save data into csv file

#### 3 - Creating the dashboard using Power BI

For creating dashboard i used Power BI an powerful tool and interactive data visualization software product developed by Microsoft

#### What i persented in my dashboard :

*   Total Expense of travels
*   Total Hours of travels
*   Total kilometers traveled
*   Total stars givin
*   Total cost of travels (dynamic in year and month)
*   Quality of my travels (How many stars were my tavels mostly ?)
*   Where was the most expensive travels for me ?
*   In which cities did I use snapp the most ?
*   My travels table (all of my travels (dynamic by sorting) )

#### Libraries and software used :

*   [Power BI](https://powerbi.microsoft.com/en/)
*   [selenium](https://www.selenium.dev/)
*   [pandas](https://pandas.pydata.org/)
*   [persiantools](https://pypi.org/project/persiantools/)
*   [deep-translator](https://pypi.org/project/deep-translator/)



Scraping and Dashboarding My Travel History from a Ride-Hailing Application

##### [Project Github repository](https://github.com/meysamraz/Google-Maps-Amsterdam-restaurants-analysis)

If you have watched the Dark series produced by Netflix, you know that understanding this series and the relationship between the characters can be a bit challenging :D . In this project, I scraped biographies of serial characters and used NLP and Graph Analysis to determine the relationship between serial characters and their importance.

![](/images/graph_chars.gif)

![](/images/comm_chars.gif)

## Project Overview :

### 1- Scraping characters biography

![](/images/bios-8b80016d.png)

### 2- Extracting .text Files and Combine Them All Together

### 3- Extracting Relationships from Characters Biography

### 4- Extracting Characters Names and Edging Based on Their Relationships

### 5- Ploting Relationships Graph

*   ### 🟣 Most Important

*   ### 🔴 Semi Important

*   ### 🟡 Moderate Important

*   ### 🟢 Secondary characters

*   ### 🔵 NPCs

![](/images/Screenshot%20(1621\).png)

### 6- Who Are The Top 10 Most Important Charecters in Tv Show

![](/images/plt1-f8246c5e.png)

### 7- Who Are The Top Top 10 Intermediarie Charecters in Dark

![](/images/plt3.png)

### 8- Who Are The Top 10 Communicator in Dark

![](/images/plt4-11d247c7.png)

### 9- Detecting Communities Between the Characters

![](/images/Screenshot%20(1623\).png)

## Libraries used in the project

*   [pandas](https://pandas.pydata.org/)

*   [matplotlib](https://matplotlib.org/)

*   [pyvis](https://pypi.org/project/pyvis/)

*   [selenium](https://www.selenium.dev/)

*   [spacy](https://spacy.io/)

*   [community](https://pypi.org/project/community/)

*   [selenium](https://www.selenium.dev/)

*   [networkx](https://networkx.org/)


Graph Analysis and Relationship Extraction from Dark Netflix Series

Project Github repository

Project Overview :

1- Scraping characters biography

2- Extracting .text Files and Combine Them All Together

3- Extracting Relationships from Characters Biography

4- Extracting Characters Names and Edging Based on Their Relationships

5- Ploting Relationships Graph

🟣 Most Important

🔴 Semi Important

🟡 Moderate Important

🟢 Secondary characters

🔵 NPCs

6- Who Are The Top 10 Most Important Charecters in Tv Show

7- Who Are The Top Top 10 Intermediarie Charecters in Dark

8- Who Are The Top 10 Communicator in Dark

9- Detecting Communities Between the Characters

Libraries used in the project

Read next

Scraping & EDA Amsterdam Restaurants and Cafés on Google Maps

Predicting Laptop Prices: An End-to-End Project Using E-commerce Website Data

Scraping and Dashboarding My Travel History from a Ride-Hailing Application