diff --git a/src/prediction/linear_predectiokn.py b/src/prediction/linear_predectiokn.py
new file mode 100644
index 0000000..7bc2a0d
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+++ b/src/prediction/linear_predectiokn.py
@@ -0,0 +1,77 @@
+import math
+
+import pandas as pd
+import numpy as np
+import plotly.express as px
+from sklearn.linear_model import LinearRegression
+
+data = pd.read_csv("/Users/sushithks/IdeaProjects/pandas/data/Population_by_year.csv")
+
+animal_data = data.loc[data['Animal'] == 'Rhino(Sumatran)']  # has to be user input
+animal_data = animal_data.loc[data['Country'] == 'World']  # has to be user input
+animal_data.sort_values("Year")
+print(animal_data)
+animal_data = animal_data.reset_index(drop=True)
+
+X = np.array(animal_data)[:, 2].reshape(-1, 1)    # fetching the 3rd column (year)
+Y = np.array(animal_data)[:, 3].reshape(-1, 1)    # fetching the 4th column (population)
+
+
+listX = [ item for elem in X for item in elem]
+listY = [ item for elem in Y for item in elem]
+# print(listX)
+# print(listY)
+
+
+animal_data.iloc[0]
+
+predict_year = []
+last_ele_array = X[len(X)-1]
+last_ele = last_ele_array[0]    # Finding the last year inorder to find out the next years
+
+count = 10
+
+for i in range (count):
+    last_ele = last_ele + 1
+    predict_year.append(last_ele)
+
+# print(predict_year)
+predict_year= np.array(predict_year).reshape(-1, 1)
+regsr = LinearRegression()
+regsr.fit(X,Y)
+
+predicted_y = regsr.predict(predict_year)
+m = regsr.coef_
+c = regsr.intercept_
+predicted_y = [math.ceil(predicted_y) for predicted_y in predicted_y]
+# print(predicted_y)
+
+print("Predicted values :")
+for i in range(count):
+    if predicted_y[i] < 0:
+        predicted_y[i] = 0
+    print(predict_year[i], "--->", predicted_y[i])
+
+predict_year= [ item for elem in predict_year for item in elem]
+# print(predict_year)
+
+
+final_x = listY + predict_year
+final_y = listY + predicted_y
+# print(final_x)
+# print(final_y)
+
+final_x = listX + predict_year
+final_y = listY + predicted_y
+
+
+fig = px.line( x = final_x ,
+               y = final_y,
+               labels={
+                   "x": "Years",
+                   "y": "Population",
+               },
+               title = 'population prediction')
+fig.show()
+print("slope (m): ", m)
+print("y-intercept (c): ", c)
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