Update README.md

Author: vmehra25

README.md

@@ -15,13 +15,13 @@ gensim installation command in jupyter notebook - !pip install --upgrade gensim
 numpy, pandas, matplotlib, scikitlearn are already included in Anaconda. <br>
 
 <h2>How to run the code</h2>
-Clone this repository and open the notebook in jupyter notebook.<br>
-Now one can run each and every cell of the notebook. Furthur details of what each section of the code contains is given below.
-The first section of the code contains preliminary work which is needed. We <b>imported the libraries</b> that are required, then <b>downloading stopwords</b> and the function for creating the <b>tf-idf vector</b>.<br>
-The next section will <b>use the imported Reuters dataset</b> and divide it into training and testing data, form the tf-idf vector from the training data. <br>
-Now the section of <b>Visualization</b> has importing gensim library, tokenising the single document text, converting the tokenised vector to pandas dataframe and then visualising the word embeddings.
-Then we move to the <b>Particle Swarm Optimization</b> section where we have a function for PSO algorithm.<br>
-The next section is for <b>Spectral Clustering</b> which will import necessary libraries, fit the data and calculate the Adjusted Random Index (ARI).<br>
-The next section is <b>our own ideas</b> which involves the idea of using <b>Principle Component Analysis(PCA) on Affinity matrix with Euclidean Distance</b>. Here we applied PCA on Affinity matrix with Euclidean Distance and then calculated the ARI for the model.<br>
-The next section has our other idea which is to use <b>Principle Component Analysis(PCA) on Affinity matrix with Gaussian Kernel</b>. Here we applied PCA on Affinity matrix with Gaussian Kernel and then calculated the ARI for the model.<br>
-The last section is the <b>Comparison of Adjusted Rand Index</b> for various models. 
+1) Clone this repository and open the notebook in jupyter notebook.<br>
+2) Now one can run each and every cell of the notebook. Furthur details of what each section of the code contains is given below in furthur steps.<br>
+3) The first section of the code contains preliminary work which is needed. We <b>imported the libraries</b> that are required, then <b>downloading stopwords</b> and the function for creating the <b>tf-idf vector</b>.<br>
+4) The next section will <b>use the imported Reuters dataset</b> and divide it into training and testing data, form the tf-idf vector from the training data. <br>
+5) Now the section of <b>Visualization</b> has importing gensim library, tokenising the single document text, converting the tokenised vector to pandas dataframe and then visualising the word embeddings.<br>
+6) Then we move to the <b>Particle Swarm Optimization</b> section where we have a function for PSO algorithm.<br>
+7) The next section is for <b>Spectral Clustering</b> which will import necessary libraries, fit the data and calculate the Adjusted Random Index (ARI).<br>
+8) The next section is <b>our own ideas</b> which involves the idea of using <b>Principle Component Analysis(PCA) on Affinity matrix with Euclidean Distance</b>. Here we applied 9) PCA on Affinity matrix with Euclidean Distance and then calculated the ARI for the model.<br>
+10) The next section has our other idea which is to use <b>Principle Component Analysis(PCA) on Affinity matrix with Gaussian Kernel</b>. Here we applied PCA on Affinity matrix with Gaussian Kernel and then calculated the ARI for the model.<br>
+11) The last section is the <b>Comparison of Adjusted Rand Index</b> for various models.