You will get a machine learning model for food freshness prediction
Project details
You will get a complete machine learning solution for predicting chicken meat freshness using sensor-based data (VOC signals). This project combines SQL, Python, and Machine Learning to deliver a full end-to-end data science pipeline.
Unlike basic scripts or partial analyses, I provide a structured workflow that includes data extraction, cleaning, exploratory data analysis, model training, evaluation, and insights interpretation.
With experience in data analysis and machine learning workflows, I focus on building clear, reproducible, and well-documented solutions that can be easily understood and used for real decision-making.
The project includes multiple classification models (Logistic Regression and Random Forest), performance evaluation, feature importance analysis, and clear visualizations to interpret results.
The final output is a fully documented machine learning pipeline with insights that help understand how sensor signals relate to meat freshness levels.
Unlike basic scripts or partial analyses, I provide a structured workflow that includes data extraction, cleaning, exploratory data analysis, model training, evaluation, and insights interpretation.
With experience in data analysis and machine learning workflows, I focus on building clear, reproducible, and well-documented solutions that can be easily understood and used for real decision-making.
The project includes multiple classification models (Logistic Regression and Random Forest), performance evaluation, feature importance analysis, and clear visualizations to interpret results.
The final output is a fully documented machine learning pipeline with insights that help understand how sensor signals relate to meat freshness levels.
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-Learn, SQLWhat's included
| Service Tiers |
Starter
$15
|
Standard
$35
|
Advanced
$60
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 4 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$5 - $15
Additional Revision
+$5
Additional Model Variation
(+ 1 Day)
+$10
Additional Scenario
(+ 1 Day)
+$5
Additional Graph/Chart
(+ 1 Day)
+$3
Model Documentation
(+ 1 Day)
+$10Frequently asked questions
About Diego
Data Analyst | ML | Python & SQL | Biochemistry & Food Industry
Quito, Ecuador - 1:31 pm local time
I have experience with data cleaning, analysis, visualization, and predictive modeling using tools such as Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn. I have developed projects related to diabetes prediction, kidney function analysis, and food quality assessment using machine learning techniques and real-world datasets.
I can help with:
• Data cleaning and preprocessing
• SQL queries and database analysis
• Python automation and data workflows
• Dashboards and data visualization
• Machine learning and predictive analysis
• Excel and reporting solutions
I am detail-oriented, adaptable, and focused on delivering accurate and efficient results. My goal is to help clients transform raw data into actionable insights that support better decision-making and business performance.
Steps for completing your project
After purchasing the project, send requirements so Diego can start the project.
Delivery time starts when Diego receives requirements from you.
Diego works on your project following the steps below.
Revisions may occur after the delivery date.
Data Collection and Understanding
Identify data types, missing values, and initial patterns to define the analysis approach.
Data Cleaning and Preprocessing
Handle missing values, encode variables, and prepare data for modeling.