diff --git a/machine_learning/simple_linear_regression.c b/machine_learning/simple_linear_regression.c
new file mode 100644
index 0000000000..36b82fb6e7
--- /dev/null
+++ b/machine_learning/simple_linear_regression.c
@@ -0,0 +1,282 @@
+/**
+ * @file simple_linear_regression.c
+ * @brief Simple Linear Regression Algorithm implemented
+ * @details
+ * this file has Simple Linear Regression Algorithm implemented
+ * it calculate intercept and slope for algorithm to predict 
+ *
+ * Note:
+ * this is a simple linear regression or
+ * single linear regression which is
+ * this code only worked for 1D Arrays and only can be applied
+ * for 1 Column of predictor variable (x)
+ * 
+ * @author [KeyzarRasya](https://github.com/KeyzarRasya)
+ */
+
+#include <assert.h>	/* assert */
+#include <stdio.h>	/* printf, perror */
+#include <stdlib.h>	/* exit, malloc */
+#include <string.h>	/* memcpy */
+#include <math.h>	/* fabs */
+
+#define EPSILON 0.0001 /* limit of tolerance for testing prediction */
+
+/*!
+ * @enum data_type
+ * an enum to storing information
+ * about data type used for performing
+ * calculation in simple_linear_regression
+ */
+enum data_types {
+    TYPE_INT,
+    TYPE_FLOAT
+};
+
+
+/*! @struct simple_linear_regression
+ * a class to store predictor (x) and target (y)
+ * variables and also for storing the result of
+ * slope and intercept
+ */
+struct simple_linear_regression {
+    void *predictor;	    	/**< used to predict the target variables */
+    void *target;		        /**< known as (y) variables or the predicted values */
+    int size;			        /**< the length of predictor and target variables */
+    float intercept;	    	/**< storing the result of intercept */ 
+    float slope;		        /**< storing the result of intercept */
+    enum data_types data_type;	/**< the type data used for predictor variables */
+};
+
+/*!
+ * Return the pointer of simple_linear_regression
+ * struct with information passed in parameters
+ *
+ * @param predictor 	array of predictors variables (x)
+ * @param target 	    array of target variables (y)
+ * @param data_type  	data type of predictor variables
+ * @param size		    size of given predictor variables
+*/
+struct simple_linear_regression *init_simple_linear_regression(
+    void *predictor, void *target, enum data_types data_type, int size) {
+
+	/* allocating memory for simple_linear_regression structi */
+    	struct simple_linear_regression *regressor = 
+            (struct simple_linear_regression*)
+            malloc(sizeof(struct simple_linear_regression));
+            
+    	if (!regressor) {
+        	perror("Failed to allocate memory");
+        	return NULL;
+    	} /* if memory failed to be allocated */
+
+    	size_t element_size = (data_type == TYPE_INT) ? sizeof(int) : sizeof(float);
+
+    	regressor->predictor = malloc(size * element_size);
+    	regressor->target = malloc(size * element_size);
+	
+	if (!regressor->predictor || !regressor->target) {
+        	perror("Failed to allocate memory");
+        	return NULL;
+    	}/* if memory at predictor and target failed to be allocated */
+
+    	memcpy(regressor->predictor, predictor, size * element_size);
+    	memcpy(regressor->target, target, size * element_size);
+
+    	regressor->data_type = data_type;
+    	regressor->size = size;
+    	regressor->intercept = 0.0;
+    	regressor->slope = 0.0;
+
+    	return regressor;
+}
+
+/*!
+ * Return sum of the array for
+ * data typed arrays
+ *
+ * @param *arr	array to be summed
+ * @param size	size of the given array 
+ */
+float sum_float(float *arr, int size) {
+    float total = 0.0;
+    
+    for (int i = 0; i < size; i++) {
+        total += arr[i];
+    }
+
+    return total;
+}
+
+/*!
+ * Returns array values that already squared
+ *
+ * @param *num	the array to be squarred
+ * @param size the size of the given arrays 
+ */
+float *square_float(float *arr, int size) {
+    float *result = malloc(size * sizeof(float));
+    
+    for (int i = 0; i < size; i++) {
+        result[i] = arr[i] * arr[i];
+    }
+
+    return result;
+}
+
+/*!
+ * Return the multiplication of predictor (x)
+ * and target (y)	
+ * 
+ * @param *x 	predictor variables
+ * @param *y 	target variables
+ * @param size 	size of given arrays
+ */
+float *calculate_x_times_y_float(float *x, float *y, int size) {
+    float *result = malloc(size * sizeof(float));
+    
+    for (int i = 0; i < size; i++) {
+        result[i] = x[i] * y[i];
+    }
+    
+    return result;
+}
+
+/*!
+ * Return value of calculation scope
+ *
+ * @param n		        length of predictor and target variables
+ * @param sum_x 	    result sum of x variables (predictor)
+ * @param sum_xy	    the result of summed x_times_y
+ * @param sum_y	        result sum of y variables (target)
+ * @param sum_x_square	the result of summed x squared
+ */
+float calculate_slope(int n, float sum_x, float sum_xy,
+                         float sum_y, float sum_x_square) {
+    float top = (n * sum_xy) - (sum_x * sum_y);
+    float bottom = (n * sum_x_square) - (sum_x * sum_x);
+    
+    if (bottom == 0) {
+        printf("\nError: Division by zero in slope calculation!\n");
+        return 0;
+    }/* if bottom equals zero, in mathematics this return undefined */
+
+    return top / bottom;
+}
+
+/*!
+ * Return the calculation of intercept
+ *
+ * @param x_mean	mean of x variables (predictor)
+ * @param y_mean	mean of y variables (target)
+ * @param slope	    slope values 
+ */
+float calculate_intercept(float x_mean, float y_mean, float slope) {
+    return y_mean - (slope * x_mean);
+}
+
+/*!
+ * ---------Training-------------
+ * 1. Checking the simple_linear_regression data types
+ * 2. Calculating all the variables are needed
+ * 3. Calculating slope and intercept
+ * 4. store slope and intercept values to simple_linear_regression struct
+ *
+ * @param regressor	struct of simple_linear_regression
+ */
+void train(struct simple_linear_regression *regressor) {
+    if (regressor->data_type == TYPE_INT) {
+        int *x = (int *)regressor->predictor;
+        int *y = (int *)regressor->target;
+
+        int sum_x = sum_float((float *)x, regressor->size);
+        int sum_y = sum_float((float *)y, regressor->size);
+        float *x_square = square_float((float *)x, regressor->size);
+        float *x_times_y = calculate_x_times_y_float((float *)x, 
+                                                    (float *)y, regressor->size);
+        float sum_x_square = sum_float(x_square, regressor->size);
+        float sum_xy = sum_float(x_times_y, regressor->size);
+
+        float x_mean = (float)sum_x / regressor->size;
+        float y_mean = (float)sum_y / regressor->size;
+
+        regressor->slope = calculate_slope(regressor->size, sum_x,
+                                             sum_xy, sum_y, sum_x_square);
+        regressor->intercept = calculate_intercept(x_mean, 
+                                                    y_mean, regressor->slope);
+
+        free(x_square);
+        free(x_times_y);
+    } /*if simple_linear_regression data type is integer */
+    else {
+        float *x = (float *)regressor->predictor;
+        float *y = (float *)regressor->target;
+
+        float sum_x = sum_float(x, regressor->size);
+        float sum_y = sum_float(y, regressor->size);
+        float *x_square = square_float(x, regressor->size);
+        float *x_times_y = calculate_x_times_y_float(x, y, regressor->size);
+        float sum_x_square = sum_float(x_square, regressor->size);
+        float sum_xy = sum_float(x_times_y, regressor->size);
+
+        float x_mean = sum_x / regressor->size;
+        float y_mean = sum_y / regressor->size;
+
+        regressor->slope = calculate_slope(regressor->size, sum_x, 
+                                            sum_xy, sum_y, sum_x_square);
+        regressor->intercept = calculate_intercept(x_mean, y_mean, 
+                                                    regressor->slope);
+
+        free(x_square);
+        free(x_times_y);
+    } /* if simple_linear_regression data_type is float */
+}
+
+/*!
+ * Returning the result of simple_linear_regression prediction
+ *
+ * @param regressor	used simple_linear_regression struct
+ * @param pred		value that want to be predicted
+ */
+float predict(struct simple_linear_regression *regressor,
+             float pred) {
+    return regressor->intercept + (regressor->slope * pred);
+}
+
+/*!
+ * A function to test simple_linear_regression
+ * prediction process
+ * 
+ * x variables is product price
+ * y variables is amount of quality sold
+ */
+void test(){
+	float x[] = {50000.0, 55000.0, 60000.0, 65000.0, 70000.0};
+	float y[] = {20.0, 18.0, 15.0, 12.0, 10.0};
+
+    	struct simple_linear_regression *regressor = 
+            init_simple_linear_regression(x, y, TYPE_FLOAT, sizeof(x)/sizeof(float));
+
+	train(regressor);
+
+	float pred = 75000.0;
+	float prediction = predict(regressor, pred);
+
+	printf("\nPredicted value for product price %.2f is %f\n",
+             pred, prediction);
+	
+	assert(fabs(prediction - 7.200008) < EPSILON);
+	printf("Assertion Passed\n");
+	free(regressor->predictor);
+	free(regressor->target);
+	free(regressor);
+}
+
+/*!
+ * main function to call the
+ * test functions
+ */
+int main() {
+	test();
+	return 0;
+}