Polycystic ovary syndrome, or PCOS is a very common type of Heterogenous endocrine disorder in females of reproductive age. Women with PCOS have a hormonal imbalance and metabolism problems that may affect their overall health.

• Infrequent, irregular, absent or prolonged menstrual periods
• Male pattern baldness or thinning hair
• Acne, oily skin and dandruff
• Anxiety or depression
• Cysts in the ovaries
• Dark patches on the skin
• Weight gain
• Infertility
• Pelvic pain
• Hirsutism
• Sleep apnea, etc

For this project, I have developed a predictive system by comparing 5 different Machine Learning models accuracy and finding the most efficient model and using it for a more precise diagnosis.

NOTE: Github doesn’t render iframes at the moment. Plotly Charts present in the code file donot show up in github. Click on PCOS Diagnosis System to view the plotly interactive graphs using nbviewer.

1. Importing Libraries
2. Loading Data
3. Data Preprocessing
4. Variable Description
5. Exploratory Data Analysis
6. Data Modeling
7. Result

• PCOS_data_without_infertility.xlsx -> Excel Data File
• PCOS_infertility.csv -> Comma Separated Values or CSV Data File
• PCOS Diagnosis System -> Project Source Code File