Finance and Accounting Automation - Tax Filing Reminder System

 Notes:

• What problem does it solve?: Helps users and businesses remember tax filing deadlines to avoid penalties.

• How can businesses benefit from customizing the code?: Users can adjust tax filing deadlines and customize the reminder message.

• How can businesses adopt the solution further?: Integrate the tool with tax preparation systems to automate the entire filing process.

Actual Python Code:

import smtplib

from email.mime.text import MIMEText

from datetime import datetime, timedelta

class TaxReminder:

    def __init__(self, email, deadline_dates):

        self.email = email

        self.deadline_dates = [datetime.strptime(date, "%Y-%m-%d") for date in deadline_dates]

    def send_email(self, subject, body):

        msg = MIMEText(body)

        msg['Subject'] = subject

        msg['From'] = 'noreply@company.com'

        msg['To'] = self.email

        

        with smtplib.SMTP('smtp.gmail.com', 587) as server:

            server.starttls()

            server.login('your_email@example.com', 'your_password')

            server.sendmail('noreply@company.com', self.email, msg.as_string())

    def check_reminders(self):

        today = datetime.today()

        for deadline in self.deadline_dates:

            days_left = (deadline - today).days

            if days_left <= 7 and days_left >= 0:

                self.send_email(f"Tax Filing Reminder: {deadline.strftime('%Y-%m-%d')}",

                                 f"Your tax filing deadline is approaching on {deadline.strftime('%Y-%m-%d')}! You have {days_left} days left.")

                print(f"Reminder sent for deadline {deadline.strftime('%Y-%m-%d')}")

                

# Example usage

tax_dates = ["2025-04-15", "2025-06-30"]

tax_reminder = TaxReminder("user@example.com", tax_dates)

tax_reminder.check_reminders()

Finance and Accounting Automation - Cash Flow Forecasting Tool

 Notes:

• What problem does it solve?: Automates cash flow forecasting, predicting future cash inflows and outflows.

• How can businesses benefit from customizing the code?: Businesses can customize cash flow forecasting based on their seasonal trends, integrate it with accounting software, and track reserves.

• How can businesses adopt the solution further?: Extend the tool to track outstanding invoices and forecast cash flow adjustments.

Actual Python Code:

import numpy as np

import pandas as pd

class CashFlowForecasting:

    def __init__(self, inflows, outflows):

        self.inflows = inflows

        self.outflows = outflows

    def forecast(self, periods):

        forecasted_inflows = np.array(self.inflows) * np.random.normal(1, 0.05, periods)

        forecasted_outflows = np.array(self.outflows) * np.random.normal(1, 0.05, periods)

        net_cash_flow = forecasted_inflows - forecasted_outflows

        cumulative_cash_flow = np.cumsum(net_cash_flow)

        return pd.DataFrame({

            'Period': range(1, periods + 1),

            'Forecasted Inflows': forecasted_inflows,

            'Forecasted Outflows': forecasted_outflows,

            'Net Cash Flow': net_cash_flow,

            'Cumulative Cash Flow': cumulative_cash_flow

        })

    def display_forecast(self, periods):

        df = self.forecast(periods)

        print(df)

# Example usage

inflows = [10000, 12000, 15000]

outflows = [8000, 9000, 10000]

cash_flow = CashFlowForecasting(inflows, outflows)

cash_flow.display_forecast(periods=12)

Finance and Accounting Automation - Profit Margin Calculator

Notes:

• What problem does it solve?: Calculates the profit margin for various products/services, helping businesses gauge profitability.

• How can businesses benefit from customizing the code?: Businesses can track profitability for individual products, adjust for fixed and variable costs.

• How can businesses adopt the solution further?: Integrate with sales data to track real-time profit margins on products or services.

Actual Python Code:

class ProfitMarginCalculator:

    def __init__(self, cost_price, selling_price):

        self.cost_price = cost_price

        self.selling_price = selling_price

    def calculate_profit_margin(self):

        margin = (self.selling_price - self.cost_price) / self.selling_price * 100

        return margin

    def display_profit_margin(self):

        margin = self.calculate_profit_margin()

        print(f"Profit Margin: {margin:.2f}%")

# Example usage

product = ProfitMarginCalculator(cost_price=50, selling_price=120)

product.display_profit_margin()

 

Finance and Accounting Automation - Loan Amortization Calculator

 Notes:

• What problem does it solve?: Automatically calculates loan amortization schedules, breaking down each payment into principal and interest components.

• How can businesses benefit from customizing the code?: Customize loan parameters like payment frequency, loan term, and interest rates.

• How can businesses adopt the solution further?: Integrate the tool with loan management software or extend it to generate reports.

Actual Python Code:

import numpy as np

import pandas as pd

class LoanAmortization:

    def __init__(self, loan_amount, interest_rate, periods):

        self.loan_amount = loan_amount

        self.interest_rate = interest_rate / 100 / 12  # Monthly interest rate

        self.periods = periods

    def calculate_amortization(self):

        monthly_payment = self.loan_amount * self.interest_rate / (1 - (1 + self.interest_rate) ** -self.periods)

        schedule = []

        balance = self.loan_amount

        for period in range(1, self.periods + 1):

            interest = balance * self.interest_rate

            principal = monthly_payment - interest

            balance -= principal

            schedule.append([period, principal, interest, monthly_payment, balance])

        return pd.DataFrame(schedule, columns=['Period', 'Principal Payment', 'Interest Payment', 'Total Payment', 'Remaining Balance'])

    def display_amortization_schedule(self):

        df = self.calculate_amortization()

        print(df)

# Example usage

loan = LoanAmortization(loan_amount=10000, interest_rate=5, periods=12)

loan.display_amortization_schedule()

Finance and Accounting Automation - Account Reconciliation Tool

 Notes:

• What problem does it solve?: Automates account reconciliation by comparing transactions from two different sources (e.g., bank and ledger).

• How can businesses benefit from customizing the code?: Businesses can define reconciliation rules based on their specific chart of accounts or transaction types.

• How can businesses adopt the solution further?: Integrate with banking APIs or accounting software for automatic transaction matching.

Actual Python Code:

import pandas as pd

class AccountReconciliation:

    def __init__(self, bank_transactions, ledger_transactions):

        self.bank_transactions = pd.DataFrame(bank_transactions)

        self.ledger_transactions = pd.DataFrame(ledger_transactions)

    def reconcile(self):

        matched = pd.merge(self.bank_transactions, self.ledger_transactions, on=['Amount', 'Description'], how='inner')

        unmatched_bank = self.bank_transactions[~self.bank_transactions['Transaction_ID'].isin(matched['Transaction_ID'])]

        unmatched_ledger = self.ledger_transactions[~self.ledger_transactions['Transaction_ID'].isin(matched['Transaction_ID'])]

        

        print("Matched Transactions:")

        print(matched)

        print("\nUnmatched Bank Transactions:")

        print(unmatched_bank)

        print("\nUnmatched Ledger Transactions:")

        print(unmatched_ledger)

        return matched, unmatched_bank, unmatched_ledger

# Example usage

bank_transactions = [{'Transaction_ID': 1, 'Amount': 100, 'Description': 'Payment to Vendor'},

                     {'Transaction_ID': 2, 'Amount': 50, 'Description': 'Refund'},

                     {'Transaction_ID': 3, 'Amount': 200, 'Description': 'Payment to Vendor'}]

ledger_transactions = [{'Transaction_ID': 1, 'Amount': 100, 'Description': 'Payment to Vendor'},

                       {'Transaction_ID': 2, 'Amount': 200, 'Description': 'Payment to Vendor'},

                       {'Transaction_ID': 3, 'Amount': 50, 'Description': 'Refund'}]

reconciliation = AccountReconciliation(bank_transactions, ledger_transactions)

reconciliation.reconcile()

Finance and Accounting Automation - Financial Forecasting Model

 Notes:

• What problem does it solve?: Automates financial forecasting based on historical data.

• How can businesses benefit from customizing the code?: Businesses can adjust for seasonal trends and other factors to get more accurate predictions.

• How can businesses adopt the solution further?: Integrate with external data sources to adjust forecasts in real time.

Actual Python Code:

import pandas as pd

import numpy as np

from statsmodels.tsa.holtwinters import ExponentialSmoothing

class FinancialForecasting:

    def __init__(self, historical_data):

        self.historical_data = historical_data

    def forecast(self, forecast_period=12):

        model = ExponentialSmoothing(self.historical_data, trend='add', seasonal='add', seasonal_periods=12)

        model_fit = model.fit()

        forecast = model_fit.forecast(forecast_period)

        return forecast

    def display_forecast(self):

        forecast = self.forecast()

        print(f"Forecasted Values for the Next Periods:")

        for i, value in enumerate(forecast):

            print(f"Month {i+1}: ${value:.2f}")

# Example usage

historical_data = [12000, 12500, 13000, 13500, 14000, 14500, 15000, 15500, 16000, 16500, 17000, 17500]

forecasting = FinancialForecasting(historical_data)

forecasting.display_forecast()

Finance and Accounting Automation - Budget Planning Tool

 Notes:

• What problem does it solve?: Helps businesses plan and track their budgets, comparing actual vs. planned expenses.

• How can businesses benefit from customizing the code?: Businesses can tailor budget categories, add multiple forecasts, and track variances.

• How can businesses adopt the solution further?: Extend to integrate with accounting software to track spending in real-time.

Actual Python Code:

import pandas as pd

class BudgetPlanner:

    def __init__(self, categories, planned_amounts, actual_amounts):

        self.categories = categories

        self.planned_amounts = planned_amounts

        self.actual_amounts = actual_amounts

    def create_budget_df(self):

        data = {

            'Category': self.categories,

            'Planned Amount': self.planned_amounts,

            'Actual Amount': self.actual_amounts,

            'Variance': [p - a for p, a in zip(self.planned_amounts, self.actual_amounts)]

        }

        return pd.DataFrame(data)

    def analyze_budget(self):

        df = self.create_budget_df()

        print("Budget Overview:")

        print(df)

        total_planned = df['Planned Amount'].sum()

        total_actual = df['Actual Amount'].sum()

        total_variance = df['Variance'].sum()

        print(f"\nTotal Planned: ${total_planned:.2f}")

        print(f"Total Actual: ${total_actual:.2f}")

        print(f"Total Variance: ${total_variance:.2f}")

        return df

# Example usage

categories = ['Marketing', 'Salaries', 'Operations']

planned = [5000, 30000, 12000]

actual = [4500, 29000, 12500]

budget = BudgetPlanner(categories, planned, actual)

budget.analyze_budget()

Finance and Accounting Automation - Financial Statement Generator

 Notes:

• What problem does it solve?: Automates the creation of a company’s income statement and balance sheet.

• How can businesses benefit from customizing the code?: Businesses can adjust the financial categories and include additional assets/liabilities to reflect their specific financial situation.

• How can businesses adopt the solution further?: Add real-time data integration with accounting software for automated monthly or quarterly reports.

Actual Python Code:

import pandas as pd

class FinancialStatement:

    def __init__(self, revenue, expenses, assets, liabilities, equity):

        self.revenue = revenue

        self.expenses = expenses

        self.assets = assets

        self.liabilities = liabilities

        self.equity = equity

    def income_statement(self):

        net_income = self.revenue - self.expenses

        print(f"Income Statement:\nRevenue: ${self.revenue}\nExpenses: ${self.expenses}\nNet Income: ${net_income}")

        return net_income

    def balance_sheet(self):

        total_assets = sum(self.assets.values())

        total_liabilities = sum(self.liabilities.values())

        total_equity = sum(self.equity.values())

        print(f"Balance Sheet:\nTotal Assets: ${total_assets}\nTotal Liabilities: ${total_liabilities}\nTotal Equity: ${total_equity}")

        return total_assets, total_liabilities, total_equity

# Example usage

assets = {'Cash': 50000, 'Inventory': 20000, 'Property': 100000}

liabilities = {'Loans': 40000, 'Accounts Payable': 15000}

equity = {'Common Stock': 30000, 'Retained Earnings': 10000}

fs = FinancialStatement(revenue=120000, expenses=80000, assets=assets, liabilities=liabilities, equity=equity)

fs.income_statement()

fs.balance_sheet()

Finance and Accounting Automation - Invoice Due Date Reminder

 Notes:

• What problem does it solve?: It automatically sends reminders for overdue or upcoming invoices.

• How can businesses benefit from customizing the code?: Customizing reminder thresholds (e.g., 3 days before due date, 7 days after overdue) can ensure timely payments.

• How can businesses adopt the solution further?: Extend the tool to automatically generate reminders and send them via email or text message using APIs.

Actual Python Code:

from datetime import datetime, timedelta

class InvoiceReminder:

    def __init__(self, invoice_date, customer_name, email):

        self.invoice_date = datetime.strptime(invoice_date, '%Y-%m-%d')

        self.customer_name = customer_name

        self.email = email

    def send_reminder(self):

        today = datetime.today()

        due_date = self.invoice_date

        days_left = (due_date - today).days

        if days_left <= 7 and days_left >= 0:

            print(f"Reminder sent to {self.customer_name} at {self.email}: Invoice is due in {days_left} days.")

        elif days_left < 0:

            print(f"Reminder sent to {self.customer_name} at {self.email}: Invoice is overdue by {-days_left} days.")

        else:

            print(f"No reminder for {self.customer_name}: Invoice is due in {days_left} days.")

# Example usage

reminder = InvoiceReminder(invoice_date="2025-04-01", customer_name="John Doe", email="john.doe@example.com")

reminder.send_reminder()

Finance and Accounting Automation - Profit and Loss Statement

 Notes:

• What problem does it solve?: Automatically generates a profit and loss statement from given financial data.

• How can businesses benefit from customizing the code?: Businesses can adjust categories for revenue and expenses, allowing for more detailed financial analysis.

• How can businesses adopt the solution further?: Integrate the tool with financial software for dynamic, real-time P&L reports, and send periodic email reports to stakeholders.

Actual Python Code:

import pandas as pd

class ProfitAndLossStatement:

    def __init__(self, revenue, expenses):

        self.revenue = revenue

        self.expenses = expenses

    def generate_statement(self):

        net_profit = self.revenue - self.expenses

        statement = {

            'Revenue': self.revenue,

            'Expenses': self.expenses,

            'Net Profit': net_profit

        }

        return statement

    def display_statement(self):

        statement = self.generate_statement()

        print("Profit and Loss Statement:")

        print(f"Revenue: ${statement['Revenue']}")

        print(f"Expenses: ${statement['Expenses']}")

        print(f"Net Profit: ${statement['Net Profit']}")

# Example usage

pnl = ProfitAndLossStatement(revenue=100000, expenses=75000)

pnl.display_statement()

Finance and Accounting Automation - Expense Tracker

 Notes:

• What problem does it solve?: Helps businesses or individuals track and categorize expenses.

• How can businesses benefit from customizing the code?: Customize categories and set budgets for each category to better manage cash flow and cut unnecessary spending.

• How can businesses adopt the solution further?: Integrate the tool with accounting software to track expenses in real time and send alerts when expenses exceed set limits.

Actual Python Code:

import pandas as pd

class ExpenseTracker:

    def __init__(self):

        self.expenses = []

    def add_expense(self, date, category, amount):

        self.expenses.append({'Date': date, 'Category': category, 'Amount': amount})

    def get_expenses(self):

        return pd.DataFrame(self.expenses)

    def categorize_expenses(self):

        df = self.get_expenses()

        return df.groupby('Category')['Amount'].sum()

# Example usage

tracker = ExpenseTracker()

tracker.add_expense('2025-03-01', 'Marketing', 200)

tracker.add_expense('2025-03-02', 'Office Supplies', 50)

tracker.add_expense('2025-03-03', 'Marketing', 150)

print("All Expenses:")

print(tracker.get_expenses())

print("\nCategorized Expenses:")

print(tracker.categorize_expenses())

Finance and Accounting Automation - Automated Invoice Generator

 Notes:

• What problem does it solve?: Manages the invoicing process by automating the creation of invoices based on product/services and customer details.

• How can businesses benefit from customizing the code?: Businesses can automate invoice generation by integrating with their product or service management system, reducing human error.

• How can businesses adopt the solution further?: Extend the system by integrating payment gateways, providing real-time invoice tracking, or adding a feature to handle recurring payments.

Actual Python Code:

import pandas as pd

from datetime import datetime

class InvoiceGenerator:

    def __init__(self, customer_name, product_details, invoice_date=None):

        self.customer_name = customer_name

        self.product_details = product_details

        self.invoice_date = invoice_date if invoice_date else datetime.today().strftime('%Y-%m-%d')

    def generate_invoice(self):

        total_amount = sum([item['quantity'] * item['unit_price'] for item in self.product_details])

        invoice = {

            'Customer Name': self.customer_name,

            'Invoice Date': self.invoice_date,

            'Products': self.product_details,

            'Total Amount': total_amount

        }

        return invoice

    def display_invoice(self):

        invoice = self.generate_invoice()

        print("Invoice Summary:")

        print(f"Customer: {invoice['Customer Name']}")

        print(f"Invoice Date: {invoice['Invoice Date']}")

        print("Products:")

        for product in invoice['Products']:

            print(f"  {product['name']} x {product['quantity']} = ${product['quantity'] * product['unit_price']:.2f}")

        print(f"Total Amount: ${invoice['Total Amount']:.2f}")

# Example usage

product_details = [{'name': 'Laptop', 'quantity': 2, 'unit_price': 500},

                   {'name': 'Mouse', 'quantity': 3, 'unit_price': 20}]

invoice = InvoiceGenerator("John Doe", product_details)

invoice.display_invoice()

E-commerce Automation and Optimization - Automated Email Marketing Campaign

 

Notes:

• Problem: Email marketing is often not personalized.

• Benefit: Automates targeted campaigns based on user behavior.

• Adoption: Integrate with CRM and e-commerce platforms for personalized campaigns.

Code:

import smtplib

from email.mime.text import MIMEText

def send_email_campaign(email_list, subject, body):

    for email in email_list:

        msg = MIMEText(body)

        msg['Subject'] = subject

        msg['From'] = 'your_email@example.com'

        msg['To'] = email

        server = smtplib.SMTP('smtp.example.com')

        server.login('your_email@example.com', 'password')

        server.sendmail('your_email@example.com', email, msg.as_string())

        server.quit()

# Usage

email_list = ['customer1@example.com', 'customer2@example.com']

send_email_campaign(email_list, 'Spring Sale!', 'Don’t miss out on our amazing spring discounts!')

E-commerce Automation and Optimization - Product Review Sentiment Analysis

 

Notes:

• Problem: Analyzing customer feedback manually is time-consuming.

• Benefit: Automates sentiment analysis to gauge product reception.

• Adoption: Customize analysis to track positive, neutral, and negative sentiments.

Code:

from textblob import TextBlob

def analyze_review_sentiment(review):

    blob = TextBlob(review)

    return blob.sentiment.polarity

# Usage

print(analyze_review_sentiment("This product is amazing!"))

E-commerce Automation and Optimization - Sales Data Dashboard

 

Notes:

• Problem: Visualizing sales data manually is time-consuming.

• Benefit: A real-time dashboard helps managers make quick decisions.

• Adoption: Integrate with business intelligence tools for more detailed analytics.

Code:

import pandas as pd

import matplotlib.pyplot as plt

# Load sales data

sales_data = pd.read_csv('sales_data.csv')

# Create sales dashboard

plt.plot(sales_data['date'], sales_data['sales'])

plt.title('Sales Over Time')

plt.xlabel('Date')

plt.ylabel('Sales')

plt.show()

E-commerce Automation and Optimization - Customizable Discount Code Generator

Notes:

• Problem: E-commerce businesses need unique discount codes for promotions.

• Benefit: Generate custom discount codes based on user behavior or events.

• Adoption: Can be integrated with the checkout system for automatic application of discounts.

Code:

import random

import string

def generate_discount_code(length=8):

    code = ''.join(random.choices(string.ascii_uppercase + string.digits, k=length))

    return f"DISCOUNT-{code}"

# Usage

print(generate_discount_code())

 

E-commerce Automation and Optimization - Dynamic Shipping Cost Calculator

 

Notes:

• Problem: Shipping costs are often unpredictable and can lead to cart abandonment.

• Benefit: Provides real-time, dynamic shipping costs based on location.

• Adoption: Customize shipping rules based on different regions or carrier APIs.

Code:

import requests

def calculate_shipping_cost(zip_code, weight):

    api_url = f"https://shipping_api.com/calculate?zip={zip_code}&weight={weight}"

    response = requests.get(api_url)

    return response.json()['cost']

# Usage

shipping_cost = calculate_shipping_cost('12345', 2.5)  # Zip code, Weight in kg

print(shipping_cost)

E-commerce Automation and Optimization - Automated Inventory Restocking Alert

 

Notes:

• Problem: Running out of stock affects sales.

• Benefit: Automatically notifies staff when stock levels are low.

• Adoption: Can be integrated with an e-commerce system to trigger orders automatically.

Code:

import smtplib

from email.mime.text import MIMEText

def send_inventory_alert(product_name, current_stock):

    if current_stock < 10:

        subject = f"Low Stock Alert: {product_name}"

        body = f"The stock for {product_name} is running low ({current_stock} left). Please reorder."

        msg = MIMEText(body)

        msg['Subject'] = subject

        msg['From'] = 'your_email@example.com'

        msg['To'] = 'inventory_manager@example.com'

        server = smtplib.SMTP('smtp.example.com')

        server.login('your_email@example.com', 'password')

        server.sendmail('your_email@example.com', 'inventory_manager@example.com', msg.as_string())

        server.quit()

# Usage

send_inventory_alert('Product A', 5)

E-commerce Automation and Optimization - Order Fraud Detection System

 

Notes:

• Problem: Fraudulent orders can result in significant losses.

• Benefit: Automatically flags suspicious orders based on predefined rules.

• Adoption: Customize fraud detection thresholds and integrate with payment processors.

Code:

import pandas as pd

from sklearn.ensemble import RandomForestClassifier

# Load order data (including features like payment method, IP address, shipping info)

order_data = pd.read_csv('orders.csv')

# Train a model to predict fraud

X = order_data.drop('fraud', axis=1)

y = order_data['fraud']

model = RandomForestClassifier()

model.fit(X, y)

# Predict fraud on new orders

new_orders = pd.read_csv('new_orders.csv')

predictions = model.predict(new_orders)

print(predictions)

E-commerce Automation and Optimization - Sales Forecasting Tool

 

Notes:

• Problem: Predicting sales to plan inventory and marketing.

• Benefit: Businesses can anticipate demand and adjust stock levels accordingly.

• Adoption: Easily integrates with inventory management systems to automate stock replenishment.

Code:

import pandas as pd

from statsmodels.tsa.holtwinters import ExponentialSmoothing

# Load historical sales data

sales_data = pd.read_csv('sales_data.csv')

# Apply Holt-Winters forecasting

model = ExponentialSmoothing(sales_data['sales'], trend='add', seasonal='add', seasonal_periods=12)

model_fit = model.fit()

# Forecast next 12 months

forecast = model_fit.forecast(12)

print(forecast)

E-commerce Automation and Optimization - Abandoned Cart Recovery System

 

Notes:

• Problem: Abandoned carts cost e-commerce businesses sales.

• Benefit: Sends automated reminders to customers to recover abandoned carts.

• Adoption: Customize reminders based on customer data and integrate with email marketing platforms.

Code:

import smtplib

from email.mime.text import MIMEText

def send_recovery_email(customer_email, cart_items):

    subject = 'Don’t forget your cart!'

    body = f"You left the following items in your cart: {cart_items}. Complete your purchase now!"

    msg = MIMEText(body)

    msg['Subject'] = subject

    msg['From'] = 'your_email@example.com'

    msg['To'] = customer_email

    server = smtplib.SMTP('smtp.example.com')

    server.login('your_email@example.com', 'password')

    server.sendmail('your_email@example.com', customer_email, msg.as_string())

    server.quit()

# Usage

send_recovery_email('customer@example.com', ['Product 1', 'Product 2'])

E-commerce Automation and Optimization - Dynamic Product Recommendation Engine

 

Notes:

• Problem: E-commerce sites often lack personalized product recommendations.

• Benefit: Increases sales by recommending relevant products to users based on past purchases.

• Adoption: Customize recommendations to reflect user behavior, product trends, etc.

Code:

import pandas as pd

from sklearn.metrics.pairwise import cosine_similarity

# Load user and product data

user_data = pd.read_csv('user_data.csv')

product_data = pd.read_csv('product_data.csv')

# Create similarity matrix

product_features = product_data.drop('product_id', axis=1)

similarity_matrix = cosine_similarity(product_features)

# Recommend products

def recommend_products(user_id):

    user_purchases = user_data[user_data['user_id'] == user_id]['purchased_products']

    recommended = []

    for product in user_purchases:

        similar_products = similarity_matrix[product]

        recommended.extend(similar_products)

    return recommended[:5]

print(recommend_products(1))  # Recommend products for user with ID 1

E-commerce Automation and Optimization - Customer Retention Prediction Model

Notes:

• Problem: Predicting customer retention in e-commerce is challenging.

• Benefit: Helps businesses focus on retaining high-value customers, saving on marketing costs.

• Adoption: Can be integrated with CRM systems to notify teams about retention efforts.

Code:

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score

# Example data (Customer ID, Age, Purchase History, Product Types, etc.)

data = pd.read_csv('customer_data.csv')

# Train a retention prediction model

X = data.drop('retention', axis=1)

y = data['retention']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

model = RandomForestClassifier()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print("Accuracy: ", accuracy_score(y_test, predictions))

 

E-commerce Automation and Optimization - Automated Price Comparison Tool

 

Notes:

• Problem: E-commerce businesses struggle to keep up with competitor pricing.

• Benefit: Automatically compares competitor prices and adjusts your pricing strategy.

• Adoption: Users can customize price comparison parameters and integrate with dynamic pricing models to stay competitive.

Code:

import requests

from bs4 import BeautifulSoup

def get_competitor_price(url):

    response = requests.get(url)

    soup = BeautifulSoup(response.text, 'html.parser')

    price = soup.find('span', {'class': 'price'}).text

    return price

def compare_prices(product_url, competitor_urls):

    product_price = get_competitor_price(product_url)

    for competitor in competitor_urls:

        competitor_price = get_competitor_price(competitor)

        print(f"Comparing prices: Product - {product_price}, Competitor - {competitor_price}")

# Usage

product_url = 'https://example.com/product'

competitor_urls = ['https://competitor1.com/product', 'https://competitor2.com/product']

compare_prices(product_url, competitor_urls)