Advanced Analytics with Fractio API

Introduction

Welcome to the Advanced Analytics tutorial for the Fractio API. In this tutorial, we'll explore how to leverage the power of our API for predictive modeling, trend analysis, and investment optimization. We'll be using Python for this tutorial, so make sure you have it installed along with the required libraries.

Prerequisites

• Python 3.7+
• Fractio API key (Premium tier)
• Basic knowledge of Python and data analysis

Setting Up

First, let's install the required libraries:

pip install requests pandas numpy scikit-learn matplotlib

1. Fetching Historical Data

We'll start by fetching historical property data using the Fractio API:

import requests
import pandas as pd

API_KEY = 'your_api_key_here'
BASE_URL = 'https://api.fractio.com/v1'

def get_historical_data(property_id, start_date, end_date):
    url = f"{BASE_URL}/properties/{property_id}/historical"
    params = {
        'start_date': start_date,
        'end_date': end_date
    }
    headers = {'Authorization': f'Bearer {API_KEY}'}
    response = requests.get(url, params=params, headers=headers)
    return response.json()

# Example usage
property_id = '123456'
start_date = '2020-01-01'
end_date = '2023-04-30'
historical_data = get_historical_data(property_id, start_date, end_date)
df = pd.DataFrame(historical_data['data'])
print(df.head())
        

2. Trend Analysis

Now that we have our historical data, let's perform a trend analysis:

import matplotlib.pyplot as plt

# Assuming 'date' and 'price' columns exist in the DataFrame
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)

plt.figure(figsize=(12, 6))
plt.plot(df.index, df['price'])
plt.title('Property Price Trend')
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

# Calculate moving average
df['MA30'] = df['price'].rolling(window=30).mean()
df['MA90'] = df['price'].rolling(window=90).mean()

plt.figure(figsize=(12, 6))
plt.plot(df.index, df['price'], label='Price')
plt.plot(df.index, df['MA30'], label='30-day MA')
plt.plot(df.index, df['MA90'], label='90-day MA')
plt.title('Property Price Trend with Moving Averages')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()
        

3. Predictive Modeling

Let's create a simple predictive model using scikit-learn:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Prepare the data
df['days'] = (df.index - df.index.min()).days
X = df[['days']]
y = df['price']

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error: {mse}")
print(f"R-squared Score: {r2}")

# Visualize the predictions
plt.figure(figsize=(12, 6))
plt.scatter(X_test, y_test, color='blue', label='Actual')
plt.plot(X_test, y_pred, color='red', label='Predicted')
plt.title('Property Price Prediction')
plt.xlabel('Days')
plt.ylabel('Price')
plt.legend()
plt.show()
        

4. Investment Optimization

Finally, let's use the Fractio API to optimize investment strategies:

def get_investment_recommendations(budget, risk_tolerance):
    url = f"{BASE_URL}/investments/recommendations"
    params = {
        'budget': budget,
        'risk_tolerance': risk_tolerance
    }
    headers = {'Authorization': f'Bearer {API_KEY}'}
    response = requests.get(url, params=params, headers=headers)
    return response.json()

# Example usage
budget = 1000000
risk_tolerance = 'moderate'
recommendations = get_investment_recommendations(budget, risk_tolerance)

for rec in recommendations['data']:
    print(f"Property ID: {rec['property_id']}")
    print(f"Expected ROI: {rec['expected_roi']}%")
    print(f"Risk Score: {rec['risk_score']}")
    print("--------------------")
        

Conclusion

In this tutorial, we've explored advanced analytics capabilities of the Fractio API, including trend analysis, predictive modeling, and investment optimization. These tools can help you make data-driven decisions in real estate investments.

Remember to handle errors, implement proper authentication, and follow best practices when using the API in production environments.