diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..a78b9c7
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,11 @@
+# To get started with Dependabot version updates, you'll need to specify whic
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+  - package-ecosystem: "" # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"
diff --git a/README.md b/README.md
index 4da9732..9e3482e 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,10 @@
 # Machine-Learning-with-Python ![GitHub stars](https://img.shields.io/github/stars/devAmoghS/Machine-Learning-with-Python?style=for-the-badge)  ![GitHub forks](https://img.shields.io/github/forks/devAmoghS/Machine-Learning-with-Python?label=Forks&style=for-the-badge)
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=devAmoghS/Machine-Learning-with-Python&type=Date)](https://star-history.com/#devAmoghS/Machine-Learning-with-Python&Date)
+
+
 ![alt text](https://media.istockphoto.com/vectors/machine-learning-3-step-infographic-artificial-intelligence-machine-vector-id962219860?k=6&m=962219860&s=612x612&w=0&h=yricYyUqZbILMHp3IvtenS3xbRDhu1w1u5kk2az5tbo=)
 
 ## Small scale machine learning projects to understand the core concepts (order: oldest to newest)
diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 0000000..034e848
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,21 @@
+# Security Policy
+
+## Supported Versions
+
+Use this section to tell people about which versions of your project are
+currently being supported with security updates.
+
+| Version | Supported          |
+| ------- | ------------------ |
+| 5.1.x   | :white_check_mark: |
+| 5.0.x   | :x:                |
+| 4.0.x   | :white_check_mark: |
+| < 4.0   | :x:                |
+
+## Reporting a Vulnerability
+
+Use this section to tell people how to report a vulnerability.
+
+Tell them where to go, how often they can expect to get an update on a
+reported vulnerability, what to expect if the vulnerability is accepted or
+declined, etc.
diff --git a/interview_prep.md b/interview_prep.md
index 141d40a..b9f25af 100644
--- a/interview_prep.md
+++ b/interview_prep.md
@@ -86,7 +86,19 @@ This means that you automatically know the thickness of 6th book even though you
 
 This means that if you have measured (n-1) objects then the nth object has no freedom to vary. Therefore, degree of freedom is only (n-1) and not n.
 
-### 7. What are the assumptions of the normal distribution ? Why is it useful ?
+### 7. What are the assumptions of the linear regression model ? Why is it useful ?
+We can divide the basic assumptions of linear regression into two categories based on whether the assumptions are about the explanatory variables (i.e. features) or the residuals.
+
+#### Assumptions about the explanatory variables (features):
+* Linearity
+* No multicollinearity
+
+#### Assumptions about the error terms (residuals):
+* Gaussian distribution
+* Homoskedasticity
+* No autocorrelation
+* Zero conditional mean
+
 ### 8. What are the different approches to outlier detection ?  How will you handle the outliers? Why is it useful ?
 ### 9. How you assess OLS regression models ?
 Three statistics are used in Ordinary Least Squares (OLS) regression to evaluate model fit: 
diff --git a/prec_rec_curve.py b/prec_rec_curve.py
new file mode 100644
index 0000000..7bde221
--- /dev/null
+++ b/prec_rec_curve.py
@@ -0,0 +1,143 @@
+import numpy as np
+from sklearn.metrics import confusion_matrix, precision_score, recall_score
+import matplotlib.pyplot as plt
+import matplotlib.patches as ptch 
+
+# Appendix A - working with single threshold
+pred_scores = [0.7, 0.3, 0.5, 0.6, 0.55, 0.9, 0.4, 0.2, 0.4, 0.3]
+y_true = ["positive", "negative", "negative", "positive", "positive", "positive", "negative", "positive", "negative", "positive"]
+
+# To convert the scores into a class label, a threshold is used. 
+# When the score is equal to or above the threshold, the sample is classified as one class. 
+# Otherwise, it is classified as the other class. 
+# Suppose a sample is Positive if its score is above or equal to the threshold. Otherwise, it is Negative. 
+# The next block of code converts the scores into class labels with a threshold of 0.5.
+
+threshold = 0.5
+
+y_pred = ["positive" if score >= threshold else "negative" for score in pred_scores]
+print(y_pred)
+
+r = np.flip(confusion_matrix(y_true, y_pred))
+print("\n# Confusion Matrix (From Left to Right & Top to Bottom: \nTrue Positive, False Negative, \nFalse Positive, True Negative)")
+print(r)
+
+# Remember that the higher the precision, the more confident the model is when it classifies a sample as Positive.
+# Higher the recall, the more positive samples the model correctly classified as Positive.
+
+precision = precision_score(y_true=y_true, y_pred=y_pred, pos_label="positive")
+print("\n# Precision = 4/(4+1)")
+print(precision)
+
+recall = recall_score(y_true=y_true, y_pred=y_pred, pos_label="positive")
+print("\n# Recall = 4/(4+2)")
+print(recall)
+
+# Appendix B - working with multiple thresholds
+y_true = ["positive", "negative", "negative", "positive", "positive", "positive", "negative", "positive", "negative", "positive", "positive", "positive", "positive", "negative", "negative", "negative"]
+
+pred_scores = [0.7, 0.3, 0.5, 0.6, 0.55, 0.9, 0.4, 0.2, 0.4, 0.3, 0.7, 0.5, 0.8, 0.2, 0.3, 0.35]
+
+thresholds = np.arange(start=0.2, stop=0.7, step=0.05)
+
+# Due to the importance of both precision and recall, there is a precision-recall curve that shows 
+# the tradeoff between the precision and recall values for different thresholds. 
+# This curve helps to select the best threshold to maximize both metrics
+
+def precision_recall_curve(y_true, pred_scores, thresholds):
+    precisions = []
+    recalls = []
+    f1_scores = []
+    
+    for threshold in thresholds:
+        y_pred = ["positive" if score >= threshold else "negative" for score in pred_scores]
+
+        precision = precision_score(y_true=y_true, y_pred=y_pred, pos_label="positive")
+        recall = recall_score(y_true=y_true, y_pred=y_pred, pos_label="positive")
+        f1_score = (2 * precision * recall) / (precision + recall)
+        
+        precisions.append(precision)
+        recalls.append(recall)
+        f1_scores.append(f1_score)
+
+    return precisions, recalls, f1_scores
+
+precisions, recalls, f1_scores = precision_recall_curve(y_true=y_true, 
+                                             pred_scores=pred_scores,
+                                             thresholds=thresholds)
+
+print("\nRecall:: 	Precision 	:: F1-Score",)
+for p, r, f in zip(precisions, recalls, f1_scores):
+	print(round(r,4),"\t::\t",round(p,4),"\t::\t",round(f,4))
+
+# np.max() returns the max. value in the array
+# np.argmax() will return the index of the value found by np.max()
+
+print('Best F1-Score: ', np.max(f1_scores))
+idx_best_f1 = np.argmax(f1_scores)
+print('\nBest threshold: ', thresholds[idx_best_f1])
+print('Index of threshold: ', idx_best_f1)
+
+# Can disable comment to display the plot
+
+# plt.plot(recalls, precisions, linewidth=4, color="red")
+# plt.scatter(recalls[idx_best_f1], precisions[idx_best_f1], zorder=1, linewidth=6)
+# plt.xlabel("Recall", fontsize=12, fontweight='bold')
+# plt.ylabel("Precision", fontsize=12, fontweight='bold')
+# plt.title("Precision-Recall Curve", fontsize=15, fontweight="bold")
+# plt.show()
+
+# Appendix C - average precision (AP)
+precisions, recalls, f1_scores = precision_recall_curve(y_true=y_true, 
+                                             pred_scores=pred_scores, 
+                                             thresholds=thresholds)
+
+precisions.append(1)
+recalls.append(0)
+
+precisions = np.array(precisions)
+recalls = np.array(recalls)
+
+print('\nRecall ::',recalls)
+print('Precision ::',precisions)
+
+AP = np.sum((recalls[:-1] - recalls[1:]) * precisions[:-1])
+print("\nAP --", AP)
+
+# Appendix D - Intersection over Union
+
+# gt_box -- 	ground-truth bounding box
+# pred_box --	prediction bounding box 
+def intersection_over_union(gt_box, pred_box):
+
+    inter_box_top_left = [max(gt_box[0], pred_box[0]), max(gt_box[1], pred_box[1])]
+
+    print("\ninter_box_top_left:", inter_box_top_left)
+    print("gt_box:", gt_box)
+    print("pred_box:", pred_box)
+    inter_box_bottom_right = [min(gt_box[0]+gt_box[2], pred_box[0]+pred_box[2]), min(gt_box[1]+gt_box[3], pred_box[1]+pred_box[3])]
+    print("inter_box_bottom_right:", inter_box_bottom_right)
+
+    inter_box_w = inter_box_bottom_right[0] - inter_box_top_left[0]
+    print("inter_box_w:", inter_box_w)
+    inter_box_h = inter_box_bottom_right[1] - inter_box_top_left[1]
+    print("inter_box_h:", inter_box_h)
+
+    intersection = inter_box_w * inter_box_h
+    union = gt_box[2] * gt_box[3] + pred_box[2] * pred_box[3] - intersection
+    
+    iou = intersection / union
+
+    return iou, intersection, union
+
+gt_box1 = [320, 220, 680, 900]
+pred_box1 = [500, 320, 550, 700]
+
+gt_box2 = [645, 130, 310, 320]
+pred_box2 = [500, 60, 310, 320]
+
+iou1 = intersection_over_union(gt_box1, pred_box1)
+print("\nIOU1 ::", iou1)
+
+iou2 = intersection_over_union(gt_box2, pred_box2)
+print("\nIOU2 ::", iou2)
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index fb81870..8d8c8e2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,20 +1,20 @@
-Keras==2.2.4
+Keras==2.13.1
 Keras-Preprocessing==1.0.5
 PySocks==1.6.8
-Pygments==2.7.4
+Pygments==2.15.0
 Quandl==3.4.5
 asn1crypto==0.24.0
 backcall==0.1.0
 beautifulsoup4==4.6.3
-certifi==2018.8.24
+certifi==2023.7.22
 cffi==1.11.5
 chardet==3.0.4
-cryptography==3.3.2
+cryptography==44.0.1
 cycler==0.10.0
 h5py==2.9.0
-idna==2.7
+idna==3.7
 inflection==0.3.1
-ipython==7.16.3
+ipython==8.10.0
 jedi==0.13.2
 kiwisolver==1.0.1
 matplotlib==3.0.0
@@ -24,7 +24,7 @@ pandas==0.23.4
 patsy==0.5.0
 pexpect==4.6.0
 pickleshare==0.7.5
-pip==21.1
+pip==23.3
 ptyprocess==0.6.0
 pyOpenSSL==18.0.0
 pycparser==2.19
@@ -32,13 +32,13 @@ pyparsing==2.2.1
 python-dateutil==2.7.3
 pytz==2018.5
 requests>=2.20.0
-scikit-learn==0.20.0
-scipy==1.1.0
+scikit-learn==1.5.0
+scipy==1.10.0
 seaborn==0.9.0
-setuptools==40.2.0
+setuptools==70.0.0
 six==1.11.0
 statsmodels==0.9.0
-tornado==5.1.1
+tornado==6.4.2
 traitlets==4.3.2
 wcwidth==0.1.7
-wheel==0.31.1
+wheel==0.38.1