Atm account number

Beginner_Projects/ATM Interface/ATM.py

@@ -7,6 +7,9 @@ def display_menu():
     print("3. Withdraw Money")
     print("4. Exit")
 
+def account_number():
+    account_number=input("enter your account number:")
+    return account_number
 
 def check_balance(balance):
     print(f"\nYour current balance is: ${balance}")
@@ -44,7 +47,7 @@ def atm_interface():
     balance = 5000.00  # Initial balance
     while True:
         display_menu()
-        choice = input("\nEnter your choice (1-4): ")
+        choice = int(input("\nEnter your choice (1-4): "))
 
         if choice == '1':
             check_balance(balance)
@@ -60,4 +63,5 @@ def atm_interface():
 
 
 # Run the ATM interface
+print(account_number())
 atm_interface()

Beginner_Projects/Amazon product availbility checker/amazon.py → Beginner_Projects/Amazon product availability checker/amazon.py

@@ -1,34 +1,34 @@
-import requests
-from bs4 import BeautifulSoup
-
-
-def check_amazon_availability(product_url):
-    headers = {
-        "User-Agent": "Your User Agent Here"  # Replace with a valid user agent string
-    }
-
-    try:
-        response = requests.get(product_url, headers=headers)
-        response.raise_for_status()
-
-        soup = BeautifulSoup(response.content, "html.parser")
-
-        title = soup.find("span", {"id": "productTitle"}).get_text(strip=True)
-        availability = soup.find(
-            "span", {"class": "a-declarative", "data-asin": True}
-        ).get_text(strip=True)
-
-        if "out of stock" in availability.lower():
-            print(f"{title} is currently out of stock on Amazon.")
-        else:
-            print(f"{title} is available on Amazon.")
-
-    except requests.exceptions.HTTPError as http_err:
-        print(f"HTTP error occurred: {http_err}")
-    except requests.exceptions.RequestException as req_err:
-        print(f"Request error occurred: {req_err}")
-
-
-if __name__ == "__main__":
-    product_url = "YOUR_PRODUCT_URL_HERE"
-    check_amazon_availability(product_url)
+import requests
+from bs4 import BeautifulSoup
+
+
+def check_amazon_availability(product_url):
+    headers = {
+        "User-Agent": "Your User Agent Here"  # Replace with a valid user agent string
+    }
+
+    try:
+        response = requests.get(product_url, headers=headers)
+        response.raise_for_status()
+
+        soup = BeautifulSoup(response.content, "html.parser")
+
+        title = soup.find("span", {"id": "productTitle"}).get_text(strip=True)
+        availability = soup.find(
+            "span", {"class": "a-declarative", "data-asin": True}
+        ).get_text(strip=True)
+
+        if "out of stock" in availability.lower():
+            print(f"{title} is currently out of stock on Amazon.")
+        else:
+            print(f"{title} is available on Amazon.")
+
+    except requests.exceptions.HTTPError as http_err:
+        print(f"HTTP error occurred: {http_err}")
+    except requests.exceptions.RequestException as req_err:
+        print(f"Request error occurred: {req_err}")
+
+
+if __name__ == "__main__":
+    product_url = "YOUR_PRODUCT_URL_HERE"
+    check_amazon_availability(product_url)

Beginner_Projects/E-Commerce/README.md

@@ -0,0 +1,53 @@
+# 🛒 E-Commerce Project
+
+## 📌 About
+This is a simple **E-Commerce project** implemented in Python.  
+It allows users to explore a mock e-commerce system where products, customers, and orders are managed.  
+The project is designed for beginners who want to practice **file handling, data manipulation, data cleaning using pandas , data visualization using matplotlib and Python basics**.
+
+---
+
+## ⚙️ Features
+- Add and view **customers**
+- Add and view **products**
+- Place and track **orders**
+- Simple **data analysis** (like total sales, most purchased product)
+
+---
+
+## 🛠️ Tech Stack
+- **Language:** Python  
+- **Libraries:**  
+  - `pandas` → for data handling  
+  - `matplotlib` → for visualizations  
+
+---
+
+## 🚀 How to Run
+
+1. Clone the repository (or download the project folder).  
+   ```bash
+   git clone https://github.com/UTSAVS26/PyVerse.git
+   ```
+
+2. Navigate to the project folder.
+    ```bash
+    cd PyVerse/beginner_projects/ecommerce
+    ```
+
+3. Run the script.
+    ```bash
+    python e-commerce.py
+    ```
+
+---
+
+## 📚 Learning Outcomes
+
+- File Handling (CSV/JSON)
+- Data Manipulation using Pandas
+- Data Visualization using Matplotlib
+- Object-Oriented Programming in Python
+
+# HARSHI GUPTA 
+- GSSOC'25 

Beginner_Projects/E-Commerce/e-commerce.py

@@ -0,0 +1,176 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime as date
+
+class E_COMMERCE:
+    def __init__(self,customer_path="data/customers.csv", product_path="data/products.csv", order_path="data/orders.csv"):
+        self.customers=pd.read_csv(customer_path)
+        self.product=pd.read_csv(product_path)
+        self.orders=pd.read_csv(order_path)
+
+    def calculate_revenue(self):
+       self.orders['quantity'] = pd.to_numeric(self.orders['quantity'], errors="coerce")
+       self.product['price'] = pd.to_numeric(self.product['price'], errors="coerce")
+       self.orders['quantity'].fillna(self.orders['quantity'].mean(), inplace=True)
+       self.product['price'].fillna(self.product['price'].median(), inplace=True)
+       merge = pd.merge(self.product, self.orders, on='product_id', how='inner')
+       merge['revenue'] = merge['quantity'] * merge['price']
+       return merge
+
+
+class DATA_CLEANING(E_COMMERCE):
+    def count_missing_values(self):
+        null_vales1=self.customers.isnull().sum()
+        null_values2=self.product.isnull().sum()
+        null_values3=self.orders.isnull().sum()
+        return f"{null_vales1}\n \n{null_values2} \n \n {null_values3}"
+
+    def missing_values(self):
+        self.product['category'].fillna(self.product['category'].value_counts().idxmax(),inplace=True)
+        self.orders['quantity'].fillna(self.orders['quantity'].mean(),inplace=True)
+        return f"{self.product} \n \n {self.orders}"
+
+    def standardize(self):
+        self.product['category']=self.product['category'].str.lower().str.strip()
+        mapping={"homeapplncs":"home appliances",
+                 "home appliances":"home appliances",
+                 "clothng":"clothing",
+                 "clothing":"clothing",
+                 "bookss":"books",
+                 "books":"books",
+                 "electrnics":"electronics",
+                 "electronics":"electronics",
+                 "sport":"sports",
+                 "sports":"sports"
+                 }
+        self.product['category']=self.product['category'].replace(mapping)
+        return self.product['category']
+
+    def date_time(self):
+        self.orders['order_date']=pd.to_datetime(self.orders['order_date'])
+        return self.orders['order_date']
+
+
+class MERGE(E_COMMERCE):
+    def concat(self):
+        file1=pd.merge(self.orders,self.customers,on='customer_id',how='inner')
+        file2=pd.merge(self.product,file1,on='product_id',how='inner').head(10)
+        return file2
+
+
+class FEATURE_ENGINEERING(E_COMMERCE):
+    def  revenue(self):
+        merge=self.calculate_revenue()
+        return merge[['quantity','price','revenue']]
+
+    def customer_counts(self):
+                value=(self.orders['customer_id']).value_counts().sort_index()
+                return f" {value} "
+
+    def Customer_Lifetime_Value(self):
+        merge=self.calculate_revenue()
+        CLV=merge.groupby('customer_id')['revenue'].sum()
+        return CLV
+
+    def purchase_date(self): 
+        self.orders['order_date'] = pd.to_datetime(self.orders['order_date'], errors='coerce')
+        date=self.orders.groupby('customer_id')['order_date'].agg(first_purchase='min',last_purchase='max')
+        return date
+
+class ANALYSIS(E_COMMERCE):
+     def top_revenues(self):
+        merge=self.calculate_revenue()
+        top_10=merge.groupby('customer_id')['revenue'].sum().sort_values(ascending=False).head(10)
+        return top_10
+
+     def product_category_revenue(self):
+        merge=self.calculate_revenue()
+        most_revenue=merge.groupby('category')['revenue'].sum().idxmax()
+        revenue=merge.groupby('category')['revenue'].sum().max()
+        return f"the most revenue is generated by category {most_revenue} having value {revenue}"
+
+     def comparison(self):
+         counts=self.orders.groupby('customer_id')['order_date'].count()
+         compare=counts.apply(lambda x:"repeat" if x>1 else "only one")
+         result=compare.value_counts()
+         return result
+
+
+class VISUALIZATION(E_COMMERCE):
+    def revenue_trend(self):
+        merge=self.calculate_revenue()
+        merge[['month','day','year']]=merge['order_date'].str.split("/",expand=True)
+        merge['month']=pd.to_numeric(merge['month'],errors="coerce")
+        result=merge.groupby("month")["revenue"].sum()
+        result.plot(kind="line",marker='*',label="revenue trend")
+        plt.xlabel("month")
+        plt.ylabel("revenue")
+        plt.title("monthly revenue")
+        plt.legend()
+        plt.show()
+
+    def revenue_category(self):
+        merge=self.calculate_revenue()
+        trend=merge.groupby("category")["revenue"].sum()
+        trend.plot(kind="bar",width=0.5,label="revenue of category")
+        plt.xlabel("category")
+        plt.ylabel("revenue")
+        plt.title("Revenue by category")
+        plt.show()
+
+    def customer_type_distribution(self):
+        counts=self.orders.groupby("customer_id")["order_date"].count()
+        compare=counts.apply(lambda x:"repeat" if x>1 else "one time")
+        result=compare.value_counts()
+        plt.pie(result.values,labels=result.index,autopct="%1.1f%%",colors=['yellow','red'])
+        plt.title("Customer type distribution (repeat vs one-time)")
+        plt.show()
+
+
+class EXPORT(E_COMMERCE):
+    def export_cleaned_csv(self, path="E_COMMERCE_CLEANED.csv"):    
+        merged = pd.merge(self.orders, self.customers, on='customer_id', how='inner')
+        merged = pd.merge(merged, self.product, on='product_id', how='inner')
+        merged.to_csv(path, index=False)
+        return f"Cleaned merged dataset saved at {path}"
+
+
+cleaning=DATA_CLEANING()
+merge=MERGE()
+features=FEATURE_ENGINEERING()
+analysis=ANALYSIS()
+visualization=VISUALIZATION()
+export=EXPORT()
+
+print()
+print(cleaning.count_missing_values())
+print()
+print(cleaning.missing_values())
+print()
+print(cleaning.standardize())
+print()
+print(cleaning.date_time())
+print()
+print(merge.concat())
+print()
+print(features.revenue())
+print()
+print(features.customer_counts())
+print()
+print(features.Customer_Lifetime_Value())
+print()
+print(features.purchase_date())
+print()
+print(analysis.top_revenues())
+print()
+print(analysis.product_category_revenue())
+print()
+print(analysis.comparison())
+print()
+print(visualization.revenue_trend())
+print()
+print(visualization.revenue_category())
+print()
+print(visualization.customer_type_distribution())
+print()
+print(export.export_cleaned_csv())