Python

Project Overview This project uses a GARCH(1,1) model to forecast the future daily volatility of the Nasdaq Composite Index (^IXIC). The goal is to estimate how much price movement (volatility) to expect in the coming days, which is useful for risk management, trading strategies, and options analysis. The project includes: Downloading daily Nasdaq data from Yahoo Finance Calculating log returns Fitting a GARCH(1,1) model Forecasting 30 days of volatility Comparing model forecasts with historical rolling volatility Wrapping the workflow into a reusable Python class Key Outputs ✅ Nasdaq Price vs. Rolling Volatility (Last 250 Days) Rolling 30-day volatility spikes during market drops. Calm price trends are usually matched by low volatility. ...

Most real-world datasets are messy. Before you can run any analysis or build a model, you need to deal with missing values, strange outliers, inconsistent formatting, and incorrect data types. This post walks through the basics of data cleaning using pandas, one of the most popular Python libraries for data manipulation. We’ll use a dataset of Nairobi property listings as our example. It contains information like location, price, number of bedrooms, and date posted. Let’s get started. ...

This project explores a dataset of Nairobi property listings to uncover patterns in pricing based on location, property type, and features like bedrooms, bathrooms, and house size. The dataset was sourced from Kaggle and cleaned using pandas before performing exploratory data analysis (EDA) and predictive modeling. 🧹 Data Cleaning Summary We performed the following steps: Removed currency symbols and converted prices to integers Renamed columns for clarity (price to price_in_ksh, etc.) Corrected inconsistent entries (e.g., townhuse to townhouse) Handled missing values: Filled missing house_size_sqm using the median by propertytype Dropped duplicate and unused columns Filtered extreme outliers 📊 Exploratory Data Analysis (EDA) 🔹 Average Price by Location ...