Update and rename README_Project.md to README.md

Updated Dataset info in same readme

README_Project.md → README.md

@@ -15,11 +15,28 @@
 	<dt><b>Benign</b></dt><dd>'Benign' cancer is the cancer which doesn't spread and are non cancerous. In most cases, a doctor diagnosing a tumor as benign will most likely be left alone.  Benign tumors are not generally aggressive around the surrounding tissue and in some cases, may continue to grow. If the tumor continues to grow and cause discomfort by pressing against surrounding organs and causing pain, the tumor would be removed.</dd>
 	<dt><b>Malignant</b></dt><dd>'Malignant' cancer cells spread across the body making it very dangerous. Malignant tumors are aggressive and cancerous because damage the surrounding tissue and may be removed depending on the cancerous and aggressive on the severity or aggressiveness of the tumor.</dd></dl>
 - [ ]  Helps in visualization of the dataset using Maps and graphs and provide a basic idea of the dataset.
-	Eg: Heatmap<br>
+	Eg: Heatmap<br><br><br>
 	![PLOT3](https://user-images.githubusercontent.com/76547274/139423128-89d21ed4-75f1-4c4b-823a-0b8f209406d5.png)
 
 
-<br>
+<br><br>
+<h2>DataSet Column/Input required for Predictions:</h2>
+ID number(unique per record)<i>Not needed for trained, just for reference</i> 
+Diagnosis (M = malignant, B = benign)<i> Testing field</i>
+<br>Ten real-valued features are computed for each cell nucleus:
+<ol>
+<li>Radius(mean of distances from center to points on the perimeter)</li>
+<li>Texture (standard deviation of gray-scale values)</li>
+<li>Perimeter</li>
+<li>Area</li>
+<li>Smoothness (local variation in radius lengths)</li>
+<li>Compactness (perimeter² / area — 1.0)</li>
+<li>Concavity (severity of concave portions of the contour)</li>
+<li>Concave points (number of concave portions of the contour)</li>
+<li>Symmetry</li>
+<li>Fractal dimension</li></ol>
+	Each of the 10 values are further given as 3 values in a total of 30 columns, which include mean, standard error and worst values from images taken of the cell.
+
 <h3>Link for Kaggle Database:</h3> You may refer this link, this will also provide you with DataSet.<a href="https://www.kaggle.com/buddhiniw/breast-cancer-prediction/data"> 'breast-cancer-prediction' </a>(You may have to clean dataset to get it in ready to use state as few records contain 0s which may effect the prediction)<br>
 
 ## Instructions to run