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Data Analysis Foundations with Python

Project 3: Capstone Project: Building a Recommender System

Problem Statement

By now, you've accomplished a wide range of topics and successfully navigated through various complex challenges. Throughout this enriching journey of learning, you have actively engaged and demonstrated your exceptional perseverance and unwavering enthusiasm, for which we express our heartfelt gratitude.  

With the aim of providing you with a comprehensive culminating experience, we have meticulously developed this Capstone Project exclusively for you. The project centers around the creation of a cutting-edge Recommender System, which stands as one of the most captivating and omnipresent applications in the field of machine learning.

Recommender systems play an integral role in the functionality of numerous platforms that we interact with on a daily basis—just think of the likes of Netflix, Amazon, or Spotify. These systems meticulously analyze extensive datasets to provide personalized recommendations for products, movies, or songs that perfectly align with individual preferences. Now, let us direct the spotlight towards you: How would you like to embark on the exhilarating journey of creating your very own Recommender System? Sounds incredibly thrilling, doesn't it?

Objective

The aim of this project is to build a Recommender System that suggests products to users based on their historical interaction with items in an online store. The system should be capable of making personalized recommendations as well as general top-N recommendations.

Why this Problem?

Recommender systems are a critical component of many online businesses. They help drive user engagement, increase sales, and enhance customer satisfaction. They are also intellectually intriguing and cover a wide range of machine learning techniques.

Evaluation Metrics

We'll evaluate the system based on:

  1. Precision@k: The fraction of recommended items that are relevant.
  2. Recall@k: The fraction of relevant items that are recommended.
  3. F1-Score: The harmonic mean of Precision and Recall.

Data Requirements

For this project, we'll use a hypothetical dataset called product_interactions.csv containing the following columns:

  • user_id: Unique identifier for users.
  • product_id: Unique identifier for products.
  • interaction_type: Type of interaction (e.g., view, add to cart, purchase).
  • timestamp: The time of interaction.

Here's a small code snippet to generate some sample data.

import pandas as pd
import numpy as np

# Generating some sample data
np.random.seed(0)
n = 1000  # Number of interactions
user_ids = np.random.choice(range(1, 11), n)  # 10 users
product_ids = np.random.choice(range(1, 21), n)  # 20 products
interaction_types = np.random.choice(['view', 'add_to_cart', 'purchase'], n)
timestamps = pd.date_range("2021-01-01", periods=n, freq="H")

# Creating DataFrame
df = pd.DataFrame({
    'user_id': user_ids,
    'product_id': product_ids,
    'interaction_type': interaction_types,
    'timestamp': timestamps
})

# Save as CSV
df.to_csv('product_interactions.csv', index=False)

df.head()

Download here the product_interactions.csv file

Isn't this exciting? Take a deep breath, flex those coding muscles, and let's embark on this final project. You've got this!

Problem Statement

By now, you've accomplished a wide range of topics and successfully navigated through various complex challenges. Throughout this enriching journey of learning, you have actively engaged and demonstrated your exceptional perseverance and unwavering enthusiasm, for which we express our heartfelt gratitude.  

With the aim of providing you with a comprehensive culminating experience, we have meticulously developed this Capstone Project exclusively for you. The project centers around the creation of a cutting-edge Recommender System, which stands as one of the most captivating and omnipresent applications in the field of machine learning.

Recommender systems play an integral role in the functionality of numerous platforms that we interact with on a daily basis—just think of the likes of Netflix, Amazon, or Spotify. These systems meticulously analyze extensive datasets to provide personalized recommendations for products, movies, or songs that perfectly align with individual preferences. Now, let us direct the spotlight towards you: How would you like to embark on the exhilarating journey of creating your very own Recommender System? Sounds incredibly thrilling, doesn't it?

Objective

The aim of this project is to build a Recommender System that suggests products to users based on their historical interaction with items in an online store. The system should be capable of making personalized recommendations as well as general top-N recommendations.

Why this Problem?

Recommender systems are a critical component of many online businesses. They help drive user engagement, increase sales, and enhance customer satisfaction. They are also intellectually intriguing and cover a wide range of machine learning techniques.

Evaluation Metrics

We'll evaluate the system based on:

  1. Precision@k: The fraction of recommended items that are relevant.
  2. Recall@k: The fraction of relevant items that are recommended.
  3. F1-Score: The harmonic mean of Precision and Recall.

Data Requirements

For this project, we'll use a hypothetical dataset called product_interactions.csv containing the following columns:

  • user_id: Unique identifier for users.
  • product_id: Unique identifier for products.
  • interaction_type: Type of interaction (e.g., view, add to cart, purchase).
  • timestamp: The time of interaction.

Here's a small code snippet to generate some sample data.

import pandas as pd
import numpy as np

# Generating some sample data
np.random.seed(0)
n = 1000  # Number of interactions
user_ids = np.random.choice(range(1, 11), n)  # 10 users
product_ids = np.random.choice(range(1, 21), n)  # 20 products
interaction_types = np.random.choice(['view', 'add_to_cart', 'purchase'], n)
timestamps = pd.date_range("2021-01-01", periods=n, freq="H")

# Creating DataFrame
df = pd.DataFrame({
    'user_id': user_ids,
    'product_id': product_ids,
    'interaction_type': interaction_types,
    'timestamp': timestamps
})

# Save as CSV
df.to_csv('product_interactions.csv', index=False)

df.head()

Download here the product_interactions.csv file

Isn't this exciting? Take a deep breath, flex those coding muscles, and let's embark on this final project. You've got this!

Problem Statement

By now, you've accomplished a wide range of topics and successfully navigated through various complex challenges. Throughout this enriching journey of learning, you have actively engaged and demonstrated your exceptional perseverance and unwavering enthusiasm, for which we express our heartfelt gratitude.  

With the aim of providing you with a comprehensive culminating experience, we have meticulously developed this Capstone Project exclusively for you. The project centers around the creation of a cutting-edge Recommender System, which stands as one of the most captivating and omnipresent applications in the field of machine learning.

Recommender systems play an integral role in the functionality of numerous platforms that we interact with on a daily basis—just think of the likes of Netflix, Amazon, or Spotify. These systems meticulously analyze extensive datasets to provide personalized recommendations for products, movies, or songs that perfectly align with individual preferences. Now, let us direct the spotlight towards you: How would you like to embark on the exhilarating journey of creating your very own Recommender System? Sounds incredibly thrilling, doesn't it?

Objective

The aim of this project is to build a Recommender System that suggests products to users based on their historical interaction with items in an online store. The system should be capable of making personalized recommendations as well as general top-N recommendations.

Why this Problem?

Recommender systems are a critical component of many online businesses. They help drive user engagement, increase sales, and enhance customer satisfaction. They are also intellectually intriguing and cover a wide range of machine learning techniques.

Evaluation Metrics

We'll evaluate the system based on:

  1. Precision@k: The fraction of recommended items that are relevant.
  2. Recall@k: The fraction of relevant items that are recommended.
  3. F1-Score: The harmonic mean of Precision and Recall.

Data Requirements

For this project, we'll use a hypothetical dataset called product_interactions.csv containing the following columns:

  • user_id: Unique identifier for users.
  • product_id: Unique identifier for products.
  • interaction_type: Type of interaction (e.g., view, add to cart, purchase).
  • timestamp: The time of interaction.

Here's a small code snippet to generate some sample data.

import pandas as pd
import numpy as np

# Generating some sample data
np.random.seed(0)
n = 1000  # Number of interactions
user_ids = np.random.choice(range(1, 11), n)  # 10 users
product_ids = np.random.choice(range(1, 21), n)  # 20 products
interaction_types = np.random.choice(['view', 'add_to_cart', 'purchase'], n)
timestamps = pd.date_range("2021-01-01", periods=n, freq="H")

# Creating DataFrame
df = pd.DataFrame({
    'user_id': user_ids,
    'product_id': product_ids,
    'interaction_type': interaction_types,
    'timestamp': timestamps
})

# Save as CSV
df.to_csv('product_interactions.csv', index=False)

df.head()

Download here the product_interactions.csv file

Isn't this exciting? Take a deep breath, flex those coding muscles, and let's embark on this final project. You've got this!

Problem Statement

By now, you've accomplished a wide range of topics and successfully navigated through various complex challenges. Throughout this enriching journey of learning, you have actively engaged and demonstrated your exceptional perseverance and unwavering enthusiasm, for which we express our heartfelt gratitude.  

With the aim of providing you with a comprehensive culminating experience, we have meticulously developed this Capstone Project exclusively for you. The project centers around the creation of a cutting-edge Recommender System, which stands as one of the most captivating and omnipresent applications in the field of machine learning.

Recommender systems play an integral role in the functionality of numerous platforms that we interact with on a daily basis—just think of the likes of Netflix, Amazon, or Spotify. These systems meticulously analyze extensive datasets to provide personalized recommendations for products, movies, or songs that perfectly align with individual preferences. Now, let us direct the spotlight towards you: How would you like to embark on the exhilarating journey of creating your very own Recommender System? Sounds incredibly thrilling, doesn't it?

Objective

The aim of this project is to build a Recommender System that suggests products to users based on their historical interaction with items in an online store. The system should be capable of making personalized recommendations as well as general top-N recommendations.

Why this Problem?

Recommender systems are a critical component of many online businesses. They help drive user engagement, increase sales, and enhance customer satisfaction. They are also intellectually intriguing and cover a wide range of machine learning techniques.

Evaluation Metrics

We'll evaluate the system based on:

  1. Precision@k: The fraction of recommended items that are relevant.
  2. Recall@k: The fraction of relevant items that are recommended.
  3. F1-Score: The harmonic mean of Precision and Recall.

Data Requirements

For this project, we'll use a hypothetical dataset called product_interactions.csv containing the following columns:

  • user_id: Unique identifier for users.
  • product_id: Unique identifier for products.
  • interaction_type: Type of interaction (e.g., view, add to cart, purchase).
  • timestamp: The time of interaction.

Here's a small code snippet to generate some sample data.

import pandas as pd
import numpy as np

# Generating some sample data
np.random.seed(0)
n = 1000  # Number of interactions
user_ids = np.random.choice(range(1, 11), n)  # 10 users
product_ids = np.random.choice(range(1, 21), n)  # 20 products
interaction_types = np.random.choice(['view', 'add_to_cart', 'purchase'], n)
timestamps = pd.date_range("2021-01-01", periods=n, freq="H")

# Creating DataFrame
df = pd.DataFrame({
    'user_id': user_ids,
    'product_id': product_ids,
    'interaction_type': interaction_types,
    'timestamp': timestamps
})

# Save as CSV
df.to_csv('product_interactions.csv', index=False)

df.head()

Download here the product_interactions.csv file

Isn't this exciting? Take a deep breath, flex those coding muscles, and let's embark on this final project. You've got this!