diff --git a/machine_learning/ordinary_least_squares_regressor.cpp b/machine_learning/ordinary_least_squares_regressor.cpp
index 54d100300..896504e20 100644
--- a/machine_learning/ordinary_least_squares_regressor.cpp
+++ b/machine_learning/ordinary_least_squares_regressor.cpp
@@ -368,12 +368,12 @@ void ols_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 ols_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);