This project focuses on time series analysis and expense forecasting using the Prophet model and traditional regression algorithms like Decision Tree, Support Vector Regression (SVR), and Linear Regression. The main objective is to predict future expenses based on historical data. Additionally, the project involves data preprocessing, which includes standardizing features and removing outliers, and exploratory data analysis (EDA) to gain insights into the dataset.
The goal of this project is to build accurate expense forecasting models and compare the performance of different algorithms. We use historical expense data, along with relevant features, to train and evaluate the models.
To prepare the data for analysis, we performed several data preprocessing steps:
We standardized the numerical features to ensure that all features have a mean of 0 and a standard deviation of 1. This step helps prevent features with large values from dominating the model.
Outliers can adversely affect model performance. We identified and removed outliers from the dataset to create a more robust model.
We checked for and handled any missing values in the dataset using appropriate imputation techniques.
EDA was conducted to gain insights into the dataset and understand the relationships between features and expenses. We visualized the data, checked for seasonality, trends, and correlations between variables.
We used the following models for expense forecasting:
Prophet is a time series forecasting model developed by Facebook. It can capture seasonality and handle missing data effectively.
Decision Tree is a non-parametric supervised learning algorithm used for regression tasks. It can handle non-linear relationships between features and target variables.
SVR is an extension of Support Vector Machines (SVM) for regression tasks. It can effectively handle high-dimensional feature spaces and complex relationships.
Linear Regression is a simple and widely used algorithm that models the relationship between the target variable and features as a linear equation.
We evaluated the performance of each model using appropriate metrics, such as Mean Squared Error (MSE) or Root Mean Squared Error (RMSE), to measure the accuracy of the predictions. The results were compared to identify the most suitable model for expense forecasting.
In conclusion, this project employed time series analysis with the Prophet model and traditional regression algorithms for expense forecasting. Through data preprocessing and exploratory data analysis, we ensured the data was suitable for modeling. The performance comparison of different algorithms provides insights into the strengths and weaknesses of each model.
To replicate or extend this project, follow these steps:
- Clone the repository to your local machine.
- Explore the Jupyter Notebook files to access the code and analysis.
- Run the code cells to perform time series analysis, model training, and performance evaluation.