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diff --git a/Fertility/README.md b/Fertility/README.md
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+Data Science Dojo
+Copyright (c) 2016 - 2019
+
+---
+
+**Level:** Beginner
+**Recommended Use:** Regression/Classification Models
+**Domain:** Healthcare/Life
+
+## Fertility Data Set
+
+### Predict seminal quality of an indivisual
+
+
+---
+
+---
+
+This *beginner* level data set has 100 rows and 10 columns.
+The data set includes semen sample of 100 volunteers, analyzed according to the WHO 2010 criteria.
+This data set can be used to determine if it is possible to reach a diagnosis without a laboratory approach, which include expensive tests, sometime uncomfortable for the patients.
+Sperm concentration are related to socio-demographic data, environmental factors, health status, and life habits. Thes eattributes can be taken easily using a questionnaire.
+
+This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **regression/classification modelling techniques**.
+Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
+
+---
+
+### Data Dictionary
+
+| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
+|------------------- |------------------------------------------ |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |-------------- |--------------- |--------------- |
+| 1 | Season | Season in which the analysis was performed (-1: winter, -0.33: spring, 0.33: summer, 1: fall) | Quantitative | 1, -1, -0.33 | 0 |
+| 2 | Age | Age at the time of analysis. Age is between 18-36 and scaled from 0 to 1 | Quantitative | 0.64, 0.78, 1 | 0 |
+| 3 | Childish Diseases | Childish diseases i.e chicken pox, measles, mumps, polio (0: no, 1: yes) | Quantitative | 1, 0 | 0 |
+| 4 | Accident or serious trauma | Accident or serious trauma (0: no, 1: yes) | Quantitative | 1, 0 | 0 |
+| 5 | Surgical intervention | Surgical intervention (0: no, 1: yes) | Quantitative | 1, 0 | 0 |
+| 6 | High fevers in last year | High fevers in the last year (-1: less than 3 months ago, 0: more than 3 months ago, 1: no fever) | Quantitative | 0, 1, -1 | 0 |
+| 7 | Frequency of alcohol consumption | Frequency of alcohol consumption in 5 categories scaled from 0 to 1. Following are the categories in order: 1) several times a day, 2) every day, 3) several times a week, 4) once a week, 5) hardly ever or never | Quantitative | 0.2, 0.6, 1 | 0 |
+| 8 | Smoking Habit | Smoking habit (-1: never, 0: occasional, 1: daily) | Quantitative | 0, 1, -1 | 0 |
+| 9 | Number of hours spent sitting per day | Number of hours spent sitting per day. Between 0 and 16, scaled from 0 to 1 | Quantitative | 0.32, 0.83, 1 | 0 |
+| 10 | Output | Output: Result of Diagnosis (N: Normal, O: Altered) | Qualitative | N, O | 0 |
+---
+
+### Acknowledgement
+
+This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Fertiltiy Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Fertility).
+The UCI page mentions the following publication as the original source of the data set:
+
+*David Gil, Jose Luis Girela, Joaquin De Juan, M. Jose Gomez-Torres, and Magnus Johnsson. Predicting seminal quality with artificial intelligence methods*
+
diff --git a/Fertility/fertility_Diagnosis.txt b/Fertility/fertility_Diagnosis.txt
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+++ b/Fertility/fertility_Diagnosis.txt
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