diff --git a/users/rushi/logistic_regression.py b/users/rushi/logistic_regression.py
new file mode 100644
index 0000000..42c5c6f
--- /dev/null
+++ b/users/rushi/logistic_regression.py
@@ -0,0 +1,37 @@
+import numpy as np
+
+#training set
+x = np.array([45,23,31,21,20,21,24,22,31,41,26,21,33,38,16,27,28,45,17,45,45,37,21,22,43,21,28,26])
+y = np.array([1,1,0,1,1,1,0,0,0,0,0,1,0,0,1,1,0,1,1,1,1,0,0,1,0,1,0,0,1])
+
+# ages of people under 50 yesrs having cancer
+# 1 = yes; 0 = nope
+
+learn = 0.00001
+
+def sigmoid(z):
+    sig = 1 / (1 + np.exp(-(z)))
+    return sig
+
+def trainer (x,y,age):
+    intercept1 = 0
+    intercept2 = 0
+    grad1 = 0
+    grad2 = 0
+    for i in range(len(y)):
+        for j in range(len(x)):
+            grad1 = (grad1 + (sigmoid(intercept1 + intercept2*x[j]) - y[i]))
+            grad2 = (grad2 + (sigmoid(intercept1 + intercept2*x[j]) - y[i])*x[j])
+            intercept1 = intercept1 - (learn * grad1)
+            intercept2 = intercept2 - (learn*grad2)
+            return sigmoid(intercept1 + intercept2*age)
+
+ages = int(input())
+print(trainer(x,y,ages))
+
+
+
+
+
+
+