diff --git a/DIRECTORY.md b/DIRECTORY.md
index 679e6f7d8..d678276bc 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -98,6 +98,7 @@
   * [Adaline Learning](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/machine_learning/adaline_learning.cpp)
   * [Kohonen Som Topology](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/machine_learning/kohonen_som_topology.cpp)
   * [Kohonen Som Trace](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/machine_learning/kohonen_som_trace.cpp)
+  * [Ordinary Least Squares Regressor](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/machine_learning/ordinary_least_squares_regressor.cpp)
 
 ## Math
   * [Armstrong Number](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/math/armstrong_number.cpp)
@@ -145,7 +146,6 @@
   * [Ode Forward Euler](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/ode_forward_euler.cpp)
   * [Ode Midpoint Euler](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/ode_midpoint_euler.cpp)
   * [Ode Semi Implicit Euler](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/ode_semi_implicit_euler.cpp)
-  * [Ordinary Least Squares Regressor](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/ordinary_least_squares_regressor.cpp)
   * [Qr Decompose](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/qr_decompose.h)
   * [Qr Decomposition](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/qr_decomposition.cpp)
   * [Qr Eigen Values](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/qr_eigen_values.cpp)
diff --git a/numerical_methods/ordinary_least_squares_regressor.cpp b/machine_learning/ordinary_least_squares_regressor.cpp
similarity index 98%
rename from numerical_methods/ordinary_least_squares_regressor.cpp
rename to machine_learning/ordinary_least_squares_regressor.cpp
index dcbbcf8c7..896504e20 100644
--- a/numerical_methods/ordinary_least_squares_regressor.cpp
+++ b/machine_learning/ordinary_least_squares_regressor.cpp
@@ -355,7 +355,7 @@ std::vector<float> predict_OLS_regressor(std::vector<std::vector<T>> const &X,
 }
 
 /** Self test checks */
-void test() {
+void ols_test() {
     int F = 3, N = 5;
 
     /* test function = x^2 -5 */
@@ -368,12 +368,12 @@ void test() {
     // perform regression modelling
     std::vector<float> beta1 = fit_OLS_regressor(data1, Y1);
     // create test data set with same features = x, x^2, x^3
-    std::vector<std::vector<float>> test1(
+    std::vector<std::vector<float>> test_data1(
         {{-2, 4, -8}, {2, 4, 8}, {-10, 100, -1000}, {10, 100, 1000}});
     // expected regression outputs
     std::vector<float> expected1({-1, -1, 95, 95});
     // predicted regression outputs
-    std::vector<float> out1 = predict_OLS_regressor(test1, beta1);
+    std::vector<float> out1 = predict_OLS_regressor(test_data1, beta1);
     // compare predicted results are within +-0.01 limit of expected
     for (size_t rows = 0; rows < out1.size(); rows++)
         assert(std::abs(out1[rows] - expected1[rows]) < 0.01);
@@ -389,12 +389,12 @@ void test() {
     // perform regression modelling
     std::vector<float> beta2 = fit_OLS_regressor(data2, Y2);
     // create test data set with same features = x, x^2, x^3
-    std::vector<std::vector<float>> test2(
+    std::vector<std::vector<float>> test_data2(
         {{-2, 4, -8}, {2, 4, 8}, {-10, 100, -1000}, {10, 100, 1000}});
     // expected regression outputs
     std::vector<float> expected2({-104, -88, -1000, 1000});
     // predicted regression outputs
-    std::vector<float> out2 = predict_OLS_regressor(test2, beta2);
+    std::vector<float> out2 = predict_OLS_regressor(test_data2, beta2);
     // compare predicted results are within +-0.01 limit of expected
     for (size_t rows = 0; rows < out2.size(); rows++)
         assert(std::abs(out2[rows] - expected2[rows]) < 0.01);
@@ -408,7 +408,7 @@ void test() {
  * main function
  */
 int main() {
-    test();
+    ols_test();
 
     size_t N, F;