Fixing README errors

Author: Younes-Charfaoui

boston_housing/README.md

@@ -4,15 +4,18 @@ The Boston housing market is highly competitive, and you want to be the best rea
 
 This is a modified Boston housing dataset consists of 489 data points, with each data point having 3 features. This dataset is a modified version of the Boston Housing dataset found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Housing).
 
-**Dataset name** 
+**Dataset name**
+
 > housing.csv
 
 **Features**
+
 1.  `RM`: average number of rooms per dwelling
 2. `LSTAT`: percentage of the population considered the lower status
 3. `PTRATIO`: pupil-teacher ratio by the town
 
 **Target Variable**
+
 4. `MEDV`: Median value of owner-occupied homes in $1000's (Price)
 
 # Objective

height_weight/README.md

@@ -3,14 +3,17 @@
 Human Height and Weight are mostly hereditable, but lifestyles, diet, health, and environmental factors also play a role in determining an individual's physical characteristics. The dataset contains 25,000 synthetic records of human heights and weights of 18 years old children. These data were simulated based on a 1993 by a Growth Survey of 25,000 children from birth to 18 years of age recruited from Maternal and Child Health Centres (MCHC) and schools and were used to develop Hong Kong's current growth charts for weight, height, weight-for-age, weight-for-height and body mass index (BMI). See also the Major League Baseball Players Height and Weight dataset.
 
 **Dataset name** 
+
 > height_weight_small.csv
 
 **Features**
+
 1.  `Index`: id number of the row.
 2. `height`: height of the person.
 3. `weights`: weight of the person.
 
 **Target Variable**
+
 4. `weights`: weight of the person.
 
 # Objective

kc_housing/README.md

@@ -4,9 +4,11 @@ This dataset contains house sale prices for King County, which includes Seattle.
 
 
 **Dataset name** 
+
 > kc_housing.csv
 
 **Features**
+
 1. `ida`: notation for a house.
 2. `date`: Date house was sold.
 3. `price`: Price is the prediction target.

weather_prediction/README.md

@@ -3,10 +3,10 @@
 Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally for millennia and formally since the 19th century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere at a given place and using meteorology to project how the atmosphere will change. In this exercise, you will use machine learning to predict the weather. You are given a dataset of weather history, and you're asked to give a model that predicts the weather, in this dataset you're also given no description about the dataset, this will make know how to deal with a dataset that you don't know anything about it.
 
 **Dataset name** 
+
 > weather.csv
 
 **Features**
-Formatted Date
 
 1.  `Formatted Date`.
 2. `Summary`.
@@ -22,6 +22,7 @@ Formatted Date
 12. `Daily Summary`.
 
 **Target Variable**
+
 4. `Temperature`.
 
 # Objective