diff --git a/contrib/scipy/installation_features.md b/contrib/scipy/installation_features.md
index 33232dc..d541086 100644
--- a/contrib/scipy/installation_features.md
+++ b/contrib/scipy/installation_features.md
@@ -1,13 +1,25 @@
 ## Installation of Scipy
-### Install using the command:
-#### C:\Users\Your Name>pip install scipy
- You can also use a Python distribution that already has Scipy installed like Anaconda, or Spyder.
+
+You can install scipy using the command:
+
+```
+$ pip install scipy
+```
+
+You can also use a Python distribution that already has Scipy installed like Anaconda, or Spyder.
+
 ### Importing SciPy
-#### from scipy import constants
+
+```python
+from scipy import constants
+```
+
 ## Key Features of SciPy
 ### 1. Numerical Integration
-#### It helps in computing definite or indefinite integrals of functions
-```
+
+It helps in computing definite or indefinite integrals of functions
+
+```python
 from scipy import integrate
 
 #Define the function to integrate
@@ -18,13 +30,18 @@ def f(x):
 result, error = integrate.quad(f, 0, 1)
 print(result)
 ```
+
 #### Output
+
 ```
 0.33333333333333337
 ```
+
 ### 2. Optimization
-#### It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation
-```
+
+It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation
+
+```python
 from scipy.optimize import minimize
 
 # Define an objective function to minimize
@@ -35,13 +52,18 @@ def objective(x):
 result = minimize(objective, x0=0)
 print(result.x)
 ```
+
 #### Output
+
 ```
 array([-1.30644012])
 ```
+
 ### 3. Linear Algebra
-#### Solving Linear computations
-```
+
+Solving Linear computations
+
+```python
 from scipy import linalg
 import numpy as np
 
@@ -55,13 +77,18 @@ b = np.array([5, 6])
 x = linalg.solve(A, b)
 print(x)
 ```
+
 #### Output
+
 ```
 array([-4. ,  4.5])
 ```
+
 ### 4. Statistics
-#### Performing statistics functions, like here we'll be distributing the data
-```
+
+Performing statistics functions, like here we'll be distributing the data
+
+```python
 from scipy import stats
 import numpy as np
 
@@ -71,9 +98,12 @@ data = stats.norm.rvs(loc=0, scale=1, size=1000)
 # Fit a normal distribution to the data
 mean, std = stats.norm.fit(data)
 ```
+
 ### 5. Signal Processing
-#### To process spectral signals, like EEG or MEG
-```
+
+To process spectral signals, like EEG or MEG
+
+```python
 from scipy import signal
 import numpy as np
 
@@ -85,14 +115,21 @@ signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(1000)
 b, a = signal.butter(4, 0.1, 'low')
 filtered_signal = signal.filtfilt(b, a, signal)
 ```
+
 The various filters applied that are applied here, are a part of signal analysis at a deeper level.
+
 ### 6. Sparse Matrix
-#### The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created
-#### There are two types of Sparse Matrices:
-##### 1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing
-##### 2. CSR: Compressed Sparse Row, it is used for fast row slicing
+
+The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created
+
+There are two types of Sparse Matrices:
+
+1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing
+2. CSR: Compressed Sparse Row, it is used for fast row slicing
+
 #### In CSC format
-```
+
+```python
 from scipy import sparse
 import numpy as np
 
@@ -104,17 +141,22 @@ values = np.array([1, 2, 1])
 
 sparse_matrix_csc = sparse.csc_matrix((values, (row_indices, col_indices)))
 ```
+
 #### In CSR format
-```
+
+```python
 from scipy import sparse
 import numpy as np
 
 data = np.array([[0, 0], [0, 1], [2, 0]])
 sparse_matrix = sparse.csr_matrix(data)
 ```
+
 ### 7. Image Processing
-#### It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used.
-```
+
+It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used.
+
+```python
 from scipy import ndimage
 import matplotlib.pyplot as plt
 
@@ -127,4 +169,5 @@ blurred_image = ndimage.gaussian_filter(image, sigma=1)
 plt.imshow(blurred_image)
 plt.show()
 ```
-#### The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image.
+
+The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image.