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https://github.com/munas-git/website-traffic-analysis


https://github.com/munas-git/website-traffic-analysis

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README 

          
# Heat Pump Installation Website Analytics and Conversion Prediction Model



## Overview



This project focuses on analysing website traffic patterns, user behavior, and conversion rates for a heat pump installation service. It includes exploratory data analysis (EDA), data preprocessing, and the development of a machine-learning model to predict conversion rates. The goal is to identify key factors influencing customer engagement and optimise the conversion funnel.



## Table of Contents



- [Introduction](#introduction)

- [Data Sources](#data-sources)

- [Libraries Used](#libraries-used)

- [Data Wrangling](#data-wrangling)

- [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)

- [Machine Learning Model Development](#machine-learning-model-development)

- [Model Evaluation](#model-evaluation)

- [Results](#results)

- [Challenges Addressed](#challenges-addressed)

- [Recommendations](#recommendations)

- [Further Investigation](#further-investigation)

- [Contact](#contact)



## Introduction



The project aims to provide insights into how website visitors interact with a heat pump installation service's online platform. By analysing various metrics, such as website visits over time, time taken to book a design consultation, and conversion rates, the project seeks to identify opportunities for improving customer engagement and increasing the likelihood of conversion.



## Data Sources



The project utilises the following datasets:



-   `funnel_data.csv`: Contains data related to user interactions and the various stages of the conversion funnel, including first search, design consultation payments, and completion.

-   `installer_locations.csv`: Contains location data of installers, which can be used to calculate distances and analyse regional trends.

-   `property_estimates.csv`: Contains property estimates that can be linked to user data for further analysis.



## Libraries Used



The following Python libraries were used in this project:



-   **Data Wrangling**:

    -   `pandas`: For data manipulation and analysis.

    -   `calendar`: For date-related functionalities.

-   **Data Preprocessing**:

    -   `LabelEncoder`: For encoding categorical variables.

-   **Geospatial Calculations**:

    -   `math`: For calculating distances between geographical coordinates.

-   **Visualisation**:

    -   `seaborn`: For creating statistical visualisations.

    -   `matplotlib`: For creating plots and charts.

    -   `IPython.display`: For displaying images.

-   **Machine Learning**:

    -   `scikit-learn`: For model development, evaluation, and preprocessing.

    -   `imblearn`: For handling imbalanced datasets using techniques like SMOTE.

    -   `xgboost`: For training gradient boosting models.

-   **Model Evaluation**:

    -   `sklearn.metrics`: For evaluating model performance using metrics like accuracy, confusion matrix, and classification report.



## Data Wrangling



The data wrangling process involved the following steps:



1.  **Loading Data**: Reading the datasets (`funnel_data.csv`, `installer_locations.csv`, `property_estimates.csv`) using pandas.

2.  **Data Type Conversion**: Converting date columns to datetime format using `pd.to_datetime`.

3.  **Handling Missing Values**: Addressing missing data in relevant columns.

4.  **Feature Engineering**: Creating new features such as time deltas between first visit and design consultation payment.



## Exploratory Data Analysis (EDA)



The EDA process involved the following steps:



1.  **Understanding Data**: Examining the structure, data types, and missing values in the datasets.

2.  **Visualising Website Visits Over Time**: Creating time series plots to identify patterns and trends in website traffic.

3.  **Analysing Time Deltas**: Calculating and visualising the time taken from the first visit to design consultation payment.

4.  **Determining Conversion Rates**: Calculating the percentage of website visitors who booked a design consultation.



## Machine Learning Model Development



The machine learning model development process involved the following steps:



1.  **Feature Selection**: Selecting relevant features for the model.

2.  **Data Preprocessing**: Encoding categorical variables using LabelEncoder and handling imbalanced datasets using SMOTE.

3.  **Model Training**: Training various machine learning models, including Support Vector Machines (SVM), XGBoost, and Decision Trees.

4.  **Pipeline Creation**: Implementing a machine learning pipeline using scikit-learn and imblearn to streamline the process.

5.  **Cross-Validation**: Employing cross-validation techniques to ensure model robustness.



## Model Evaluation



The model evaluation process involved the following steps:



1.  **Performance Metrics**: Evaluating model performance using metrics such as accuracy, confusion matrix, and classification report.

2.  **Comparative Analysis**: Comparing the performance of different models to identify the most effective one.

3.  **Visualisation**: Visualising the results using confusion matrices and other relevant plots.



## Results



Key findings from the analysis include:



-   **Website Traffic Patterns**: Peak first visits occurred at the beginning of December 2023, followed by a progressive decrease.

-   **Time to Book Consultation**: The average time from the first visit to design consultation payment is approximately 17.71 days.

-   **Conversion Rate**: Approximately 0.98% of website visitors booked a design consultation.



## Challenges Addressed



The project addressed the following challenges:



-   **Analysing website traffic patterns over time.**

-   **Determining the time taken for users to pay for design consultations after their first visit.**

-   **Calculating the percentage of website visitors who booked a design consultation.**

-   **Understanding how conversion rates vary based on eligibility for the Boiler Upgrade Scheme grant.**



## Recommendations



Based on the analysis, the following recommendations are made:



-   **Track website visits before installation deposit**: Monitor the number of website visits before clients make an installation deposit.

-   **Track proposal downloads**: If proposal downloads are an option, track these to understand client engagement.

-   **Further investigation**: Investigate the reasons for the decline in conversion rates after the initial peak in website visits.



## Further Investigation



Further areas for investigation include:



-   **Impact of Boiler Upgrade Scheme grant eligibility on conversion rates.**

-   **Reasons for the decline in conversion rates after the initial peak in website visits.**

-   **Analysis of installer locations and their impact on service delivery.**



## Contact



For any questions or further information, please contact:



\[Einetein]



\[einsteinmunachiso@gmail.com]



\[https://www.linkedin.com/in/einstein-ebereonwu/]