From c33912d486be71615bae0319c1d1cb1dbe5f42ff Mon Sep 17 00:00:00 2001 From: Abhishek Reddy Date: Thu, 16 Jul 2026 08:17:57 +0530 Subject: [PATCH 1/3] updated PULL_REQUEST_TEMPLATE.md Had completed the task but put the contents of PULL_REQUEST_TEMPLATE.md in README.md --- PULL_REQUEST_TEMPLATE.md | 64 ++++++++++++++++++++-------------------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/PULL_REQUEST_TEMPLATE.md b/PULL_REQUEST_TEMPLATE.md index bc5bba3..16ff244 100644 --- a/PULL_REQUEST_TEMPLATE.md +++ b/PULL_REQUEST_TEMPLATE.md @@ -2,7 +2,7 @@ ## Related Issue -Closes # +Closes # 12 --- @@ -14,6 +14,7 @@ Provide a brief overview of your implementation. Implemented the Isolation Forest model for detecting anomalous mammography data - What approach did you follow? + I extracted the data from a csv file, selected the features to detect the anomaly used dropna() to eliminate rows with missing values @@ -21,6 +22,7 @@ used StandardScaler to scale features split the data into test and train (test=0.1) trained the isolation forest model used iso_forest.predict() to get the number of anomaly +used matplotlib to plot the data --- @@ -35,20 +37,12 @@ Dataset Source: OpenML ## Preprocessing -Describe any preprocessing performed. - Handled missing values with pandas' built in function df.dropna() which drops rows with NaN -Selected only first 6 rows for isolation forest model as -originally the dataset was created for classification and the 7th column contained labels +Found out using SHAP, that col 4/5/6 were the major contributors to the model and rest of the columns were just noise, omitting col 1/2/3 increased f1_score drastically -Used Standard Scaler for scaling the data for better accuracy -Examples: -- Missing value handling -- Feature scaling -- Encoding -- Feature selection +Omitted using Standard Scaler as the the data was already scaled --- @@ -59,9 +53,9 @@ List the important hyperparameters used. | Hyperparameter | Value | |---------------|-------| | n_estimators | 100 | -| contamination | 0.009 | +| contamination | 0.023 | | max_samples | 256 | -| max_features | 6 | +| max_features | 3 | | random_state | 42 | --- @@ -70,9 +64,9 @@ List the important hyperparameters used. | Metric | Value | |--------|-------| -| Precision | | -| Recall | | -| F1-score | | +| Precision |0.615| +| Recall |0.473| +| F1-score |0.535| | ROC-AUC (Optional) | | --- @@ -80,15 +74,13 @@ List the important hyperparameters used. ## Visualizations Attach **at least 2 plots** from your analysis. +--- +**Confusion Matrix** ![alt text](image.png) -Examples: -- PCA visualization -- Anomaly score distribution -- Confusion Matrix -- Correlation heatmap -- Feature distributions -- Hyperparameter comparison -- Precision/Recall/F1 comparison +--- +**Scatter Plot** +![alt text](image-1.png) +--- --- @@ -96,11 +88,19 @@ Examples: Briefly summarize: -- What worked well? -- Which hyperparameter had the biggest impact? -Contamination had the biggest impact, as it directly affects the number of anomalies detected -- Any interesting findings? -- Challenges faced (if any) +- What worked well?\ +The most important function that helped tune the model better was precision_recall_curve(), which helped me know the exact threshold value, instead of blindly going for predict() + +- Which hyperparameter had the biggest impact?\ +3 Main hyperparameters **Contamination|n_estimators|max_samples** had high impacts on f1score, especially **Contamination** and **max_features** +- Any interesting findings?\ + +- Challenges faced (if any)\ +Most of the challenge faced was getting familiar with the syntax of different libraries, and researching how to improve the f1_score took a lot of time +initial f1_score: 0.264 +final f1_score: 0.535 + +Understanding how to evaluate the model (i.e implementing the precision_score etc) was a big challenge due to different conventions among OpenML|Isolation_Forest|Precision_Score --- @@ -109,6 +109,6 @@ Contamination had the biggest impact, as it directly affects the number of anoma - [x] Code runs successfully - [ ] Notebook (`.ipynb`) included - [x] Code is well-commented -- [ ] README/documentation updated -- [ ] At least **2 plots** included -- [ ] PR is linked to the corresponding issue \ No newline at end of file +- [x] README/documentation updated +- [x] At least **2 plots** included +- [x] PR is linked to the corresponding issue \ No newline at end of file From 0d0f45e0cae944908c026189460c77fb45718155 Mon Sep 17 00:00:00 2001 From: Abhishek Reddy Date: Thu, 16 Jul 2026 08:44:43 +0530 Subject: [PATCH 2/3] Updated rest of the files There was some issue, most of the files were not pushed correctly, i pushed them in this commit --- image-1.png => anomaly_scatter_plot.png | Bin image.png => confusion_matrix.png | Bin i_forest.py | 1 + mammography.csv | 2 +- ni.py | 86 -------------------- pyproject.toml | 1 + uv.lock | 103 ++++++++++++++++++++++++ 7 files changed, 106 insertions(+), 87 deletions(-) rename image-1.png => anomaly_scatter_plot.png (100%) rename image.png => confusion_matrix.png (100%) delete mode 100644 ni.py diff --git a/image-1.png b/anomaly_scatter_plot.png similarity index 100% rename from image-1.png rename to anomaly_scatter_plot.png diff --git a/image.png b/confusion_matrix.png similarity index 100% rename from image.png rename to confusion_matrix.png diff --git a/i_forest.py b/i_forest.py index 9f60871..7bc2f3e 100644 --- a/i_forest.py +++ b/i_forest.py @@ -1,4 +1,5 @@ # new things learnt +# got familiarized with syntax of new libraries and standard practices # precision_score(y_true, y_pred) both these values must be same for binary -> same tuple import pandas as pd import numpy as np diff --git a/mammography.csv b/mammography.csv index 0557841..20f8b5e 100644 --- a/mammography.csv +++ b/mammography.csv @@ -1,4 +1,4 @@ -0.23001961,5.0725783,-0.27606055,0.83244412,-0.37786573,0.4803223,'-1' +0.23001961,5.0725783,-0.27606055,0.83244412,-0.37786573,0.4803224,'-1' 0.15549112,-0.16939038,0.67065219,-0.85955255,-0.37786573,-0.94572324,'-1' -0.78441482,-0.44365372,5.6747053,-0.85955255,-0.37786573,-0.94572324,'-1' 0.54608818,0.13141457,-0.45638679,-0.85955255,-0.37786573,-0.94572324,'-1' diff --git a/ni.py b/ni.py deleted file mode 100644 index 1507fe1..0000000 --- a/ni.py +++ /dev/null @@ -1,86 +0,0 @@ -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import seaborn as sns - -from sklearn.ensemble import IsolationForest -# from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from sklearn.metrics import precision_score - -# CSV File has been directly downloaded from OpenML - -# Convert CSV to DataFrame -df = pd.read_csv("mammography.csv", header=None) -df.columns = ['col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7'] -df['col7'] = df['col7'].astype(str).str.replace("'", "").astype(int) # - -count = df['col7'].value_counts() -print(count) -# -1 -> 10923 -# 1 -> 260 - -# Select Features -features = df[['col1', 'col2', 'col3', 'col4', 'col5', 'col6']] - -# Drop rows with any missing values - -features = features.dropna() - -# Training Parameters -n_estimators = 100 -contamination = 0.023 -sample_size = 256 - -# Feature Scaling -# X_train, X_test = train_test_split(features, test_size=0,random_state=42) - -scaler = StandardScaler() -scaler.fit_transform(features) - -# X_train_scaled = scaler.fit_transform(X_train) -# X_test_scaled = scaler.transform(X_test) - -# print(X_train_scaled) -# print(X_train_scaled.shape) - -# print(X_test_scaled) -# print(X_test_scaled.shape) - -# Training Isolation Forest Model - -iso_forest = IsolationForest(n_estimators=n_estimators, contamination=contamination, - max_samples=sample_size, random_state=42) - -iso_forest.fit(features) - - - -# Trying Prediction - -df = df.loc[features.index].copy() -df['anomaly'] = iso_forest.predict(features) # raw anomaly predictions -df['anomaly_score'] = iso_forest.decision_function(features) -anomaly_count = df['anomaly'].value_counts() -print(anomaly_count) - -# Precision Score -df['anomaly_mapped'] = df['anomaly'].map({1: 0, -1: 1}) - -precision = precision_score(df['col7'], df['anomaly_mapped']) -print(precision) - -# # Plotting Data - -# plt.figure(figsize=(50,10)) - -# normal = df[df['anomaly'] == 1] -# plt.scatter(normal.index, normal['anomaly_score'] , label='Normal') - -# anomaly = df[df['anomaly'] == -1] -# plt.scatter(anomaly.index, anomaly['anomaly_score'], label='Anomaly') - -# plt.xlabel('Instance') -# plt.ylabel('Anomaly Score') -# plt.legend() -# plt.show() \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index ae87c5d..074e7f5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,5 +6,6 @@ readme = "README.md" requires-python = ">=3.13" dependencies = [ "matplotlib>=3.11.0", + "scikit-learn>=1.9.0", "seaborn>=0.13.2", ] diff --git a/uv.lock b/uv.lock index c2d641b..2d92742 100644 --- a/uv.lock +++ b/uv.lock @@ -16,12 +16,14 @@ version = "0.1.0" source = { virtual = "." } dependencies = [ { name = "matplotlib" }, + { name = "scikit-learn" }, { name = "seaborn" }, ] [package.metadata] requires-dist = [ { name = "matplotlib", specifier = ">=3.11.0" }, + { name = "scikit-learn", specifier = ">=1.9.0" }, { name = "seaborn", specifier = ">=0.13.2" }, ] @@ -122,6 +124,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/2c/47/c99d5268f354002ce80f8d029cd9d7d872969da1de8b93d32de4dc56d6f4/fonttools-4.63.0-py3-none-any.whl", hash = "sha256:445af2eab030a16b9171ea8bdda7ebf7d96bda2df88ee182a464252f6e05e20d", size = 1164562, upload-time = "2026-05-14T12:04:29.092Z" }, ] +[[package]] +name = "joblib" +version = "1.5.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603, upload-time = "2025-12-15T08:41:46.427Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071, upload-time = "2025-12-15T08:41:44.973Z" }, +] + [[package]] name = "kiwisolver" version = "1.5.0" @@ -236,6 +247,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/60/95/1d36bddf2b7e2692c1540e78a6e5bc88bc1496b137e3e35a611f91b65ac3/matplotlib-3.11.0-cp314-cp314t-win_arm64.whl", hash = "sha256:652fb5696271d4c50f196d22a5ff4f8e4444c74f847423570d7dc0aa2bbd0159", size = 9209226, upload-time = "2026-06-12T02:29:07.033Z" }, ] +[[package]] +name = "narwhals" +version = "2.24.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/2b/1d/58946e5aab18393e793bd4add6985b95d0e01c3a2d832f38f54468b10dcd/narwhals-2.24.0.tar.gz", hash = "sha256:b5c0f684ccd9d7475b564111e319a4964abcf2baf79d3cf6b1003d06ac9b828d", size = 661143, upload-time = "2026-07-13T10:49:19.086Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7e/85/a5bfaebfd305ac18b57b0854d74e37e586809061a91fda62f0bd50c8518e/narwhals-2.24.0-py3-none-any.whl", hash = "sha256:42fdedf44e5b2ca7505630d45b4ac3058f38d8485cba9fe1652ca23152df7489", size = 461030, upload-time = "2026-07-13T10:49:17.571Z" }, +] + [[package]] name = "numpy" version = "2.5.1" @@ -412,6 +432,80 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, ] +[[package]] +name = "scikit-learn" +version = "1.9.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "joblib" }, + { name = "narwhals" }, + { name = "numpy" }, + { name = "scipy" }, + { name = "threadpoolctl" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/fa/6f/37092bdb25f712817231799fc5674d8e704066a8a70c1d2d40517e18b4ab/scikit_learn-1.9.0.tar.gz", hash = "sha256:8833266989d3a5110178a9fae30783675460724d0e1efb13b14901d2c660c557", size = 7750767, upload-time = "2026-06-02T11:54:32.706Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3c/01/cf3310626b6d48d3e9be69a1223f9180360b5e6edb045f50fade723ce494/scikit_learn-1.9.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:80746d63bd4b6eaca54d36fe5feaf4d28bb38dc6f9470f81c7cad7c40155f119", size = 8705188, upload-time = "2026-06-02T11:53:41.964Z" }, + { url = "https://files.pythonhosted.org/packages/3e/04/5acd7ae280c5f93b6ac5ef6cdec14eef4c8d1cd91d85b3292989c94d96b1/scikit_learn-1.9.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:5b934c45c252844a91d69fda3a34cff5e7307e1db10d77cb10a3980312c74713", size = 8228299, upload-time = "2026-06-02T11:53:44.817Z" }, + { url = "https://files.pythonhosted.org/packages/0c/39/ffe829a5b8ecb40a518724a997794657fdc354ada5e8fe8e64d998c0bac9/scikit_learn-1.9.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:38c3dcb9a1ffb85505ec53d54c7b4aea0cff70050425a7760c2af661ac85df05", size = 8789690, upload-time = "2026-06-02T11:53:47.461Z" }, + { url = "https://files.pythonhosted.org/packages/1f/88/8dab5de10c638c083772a6be83a3d8106ced492f74a928c8693638e5bb50/scikit_learn-1.9.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:da76d09304a4706db7cc1e3ebaa3b6b98a67365cc11d2996c4f1e58ba47df714", size = 9087723, upload-time = "2026-06-02T11:53:50.702Z" }, + { url = "https://files.pythonhosted.org/packages/20/3f/7917ca72464038f6240ec70c29f94862d08a34a74291ae4d4ec5eb8186a0/scikit_learn-1.9.0-cp313-cp313-win_amd64.whl", hash = "sha256:5808d98f15c6bf6d9d96d2348c1997392a5888ce7097e664105f930c4bca1277", size = 8184330, upload-time = "2026-06-02T11:53:53.396Z" }, + { url = "https://files.pythonhosted.org/packages/78/c7/15739eb2f61fda3c54639e9942414e5a19ad8a8d1f5a3266afad7cb7df80/scikit_learn-1.9.0-cp313-cp313-win_arm64.whl", hash = "sha256:d77f54c017633791bc0225a43e2f8d03745fdcfe4880268fcc4df15f505dec2e", size = 7840653, upload-time = "2026-06-02T11:53:56.035Z" }, + { url = "https://files.pythonhosted.org/packages/f4/7d/c9a35cf59b20a86fec24d306f1547b78dec194b08d367ce2a3e4854169d9/scikit_learn-1.9.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:9656acd4e93f74e0b66c8a36c88830a99252dfa900044d36bc2212ae89a47162", size = 8713289, upload-time = "2026-06-02T11:53:58.788Z" }, + { url = "https://files.pythonhosted.org/packages/3c/a7/552a7821597c632b907f7bfe8f36f9f572777af8ef8a48353041cf8e091a/scikit_learn-1.9.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:24360002ae845e7866522b0a5bbf690802e7bc388cac8663502e78aa98598aa2", size = 8245141, upload-time = "2026-06-02T11:54:01.694Z" }, + { url = "https://files.pythonhosted.org/packages/7d/79/f4a0c4fe9711154cddabf913471153af79056382ddc612cfe5ee0ff4b72e/scikit_learn-1.9.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5162ad10a418c8a282dde04c9aa06965de3e9a65f33c1440c0ae69bb1a09d913", size = 8847671, upload-time = "2026-06-02T11:54:04.448Z" }, + { url = "https://files.pythonhosted.org/packages/f0/af/4d72d9e475ac83719160c662619e4bf7b95c19507cd582e7d0167a3c3dae/scikit_learn-1.9.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1fea2cc5677ab49d6f5bade978c866da44957b712d92e9635e8b4f723013c3cb", size = 9118104, upload-time = "2026-06-02T11:54:07.205Z" }, + { url = "https://files.pythonhosted.org/packages/a2/d5/6a58eea2cb9abbb9b3f2bb8b2cfb3243d1152d69f442d256c7af71304769/scikit_learn-1.9.0-cp314-cp314-win_amd64.whl", hash = "sha256:64fa347efc1c839c487433e40c5144d38c336e8a2b59c81aa8660373945c2673", size = 8290674, upload-time = "2026-06-02T11:54:10.087Z" }, + { url = "https://files.pythonhosted.org/packages/65/5b/d4c879cf358f1187141cf90ced473f087183489090244f50c124a2ee478b/scikit_learn-1.9.0-cp314-cp314-win_arm64.whl", hash = "sha256:1b944b6db288f6b926e3650026ddafb988929de95d11fc2cc5fa117773c9ba42", size = 7978807, upload-time = "2026-06-02T11:54:12.769Z" }, + { url = "https://files.pythonhosted.org/packages/8a/43/bfae3121ec67ae09150d453c442c7c1cc166e9aefe056e6ab3b7728a5cfc/scikit_learn-1.9.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:4ccacf04ca5f4b492158a5f28afe0ace43f81b2571e4b9a66d34848b46128949", size = 9031941, upload-time = "2026-06-02T11:54:15.436Z" }, + { url = "https://files.pythonhosted.org/packages/75/b0/20a4546eb17f3b25d3c66df15810411c14ed5065bcfab50b53c96fb627b2/scikit_learn-1.9.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:ee1a8db2c18c08e34c7412d4b10be1cac214cd4ea7dc9715a6a327eb49a37c96", size = 8613528, upload-time = "2026-06-02T11:54:18.842Z" }, + { url = "https://files.pythonhosted.org/packages/18/3c/e440e039bb82cd19004edaaad00acbde0fb9b461083c3ecf37941c557312/scikit_learn-1.9.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:147e9329ef0e39f75d4cffa02b2aa48d827832684926cd5210d9a2cb5c57246b", size = 8855050, upload-time = "2026-06-02T11:54:21.699Z" }, + { url = "https://files.pythonhosted.org/packages/43/26/b341b8dab5998da6270a3a42c2152c578501354d36f944b5856757035ef8/scikit_learn-1.9.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5bad8f8b9950321b54c965fdcbac6c6c55e79e16646b49977bcf3668d3870a1a", size = 9097190, upload-time = "2026-06-02T11:54:24.454Z" }, + { url = "https://files.pythonhosted.org/packages/fb/de/b650b4d69b84468cfa2e28a3ff7b8103743029e6446ce1a97fe060ef688c/scikit_learn-1.9.0-cp314-cp314t-win_amd64.whl", hash = "sha256:78fc56eafd4edb9575d2d8950d1dd152061abb573341a1cb7e099fc40f6c6666", size = 8963204, upload-time = "2026-06-02T11:54:27.428Z" }, + { url = "https://files.pythonhosted.org/packages/ee/f3/ff83d76d7418112e5a61326443cdda87be3545dd8d6599c95b2481a4419e/scikit_learn-1.9.0-cp314-cp314t-win_arm64.whl", hash = "sha256:051075bda8b7aab87b1906ab3d4740a1e1224a19d7b3781a576736edc94e76aa", size = 8222661, upload-time = "2026-06-02T11:54:30.192Z" }, +] + +[[package]] +name = "scipy" +version = "1.18.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a7/25/c2700dfaf6442b4effaa91af24ebce5dc9d31bb4a69706313aae70d72cd0/scipy-1.18.0.tar.gz", hash = "sha256:67b2ad2ad54c72ca6d04975a9b2df8c3638c34ddd5b28738e94fc2b57929d378", size = 30774447, upload-time = "2026-06-19T15:01:43.456Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/05/52/9c0136c2de7ae0779b7b366447766cec6d9f0702c56bb8ffeb04c8fd3af4/scipy-1.18.0-cp313-cp313-macosx_10_15_x86_64.whl", hash = "sha256:09143f676d157d9f546d663504ef9c1becb819824f1afc018814176411942446", size = 31036107, upload-time = "2026-06-19T15:00:14.03Z" }, + { url = "https://files.pythonhosted.org/packages/02/73/0291a64843270f4efb86cdcf2ee0f2048631b65ec6b405398b2b4dbf11bf/scipy-1.18.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:5efe260f69417b97ddae455bfb5a95e8359f7f66ad7fa9522a60feb66f169520", size = 28663303, upload-time = "2026-06-19T15:00:16.819Z" }, + { url = "https://files.pythonhosted.org/packages/d3/0f/10ffa0b697a572f4e0d48b92a88895d366422f019f723e7e14a84c050dac/scipy-1.18.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:68363b7eaacd8b5dd426df56d782cc156468ac79a127a1b87ca597d6e2e82197", size = 20404960, upload-time = "2026-06-19T15:00:19.635Z" }, + { url = "https://files.pythonhosted.org/packages/7e/d2/e896cea21ba8edd6c81d4c55b1ffcc717e79698dcbebf9641b4cfb4c6622/scipy-1.18.0-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:c5557d8be5da8e41353fcd4d21491fdbab83b062fc579e94dc09a7c8ab4f669b", size = 23034074, upload-time = "2026-06-19T15:00:22.107Z" }, + { url = "https://files.pythonhosted.org/packages/ea/b2/e83ea34279a52c03374477c74006256ec78df65fc877baa4617d6de1d202/scipy-1.18.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0d13bca67c096d89fb95ced0d8921807300fce0275643aef9533cc63a0773468", size = 33942038, upload-time = "2026-06-19T15:00:24.964Z" }, + { url = "https://files.pythonhosted.org/packages/f6/af/e8fe5fb136f51e2b01678b92cb4106d10d8cd68ec147ead2e7cb0ac75398/scipy-1.18.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a46f9273dbd0eb1cefba61c9b8648b4dfe3cbc14a080176f9a73e44b8336dc7f", size = 35266390, upload-time = "2026-06-19T15:00:28.059Z" }, + { url = "https://files.pythonhosted.org/packages/3a/49/2c5cbb907b56695fc67517811d1db234dfd83381a84814ec220aded2794d/scipy-1.18.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5aba46108853ddfc77906b6557aac839d2b52e900c1d72a1180adaaab58d265f", size = 35551324, upload-time = "2026-06-19T15:00:31.014Z" }, + { url = "https://files.pythonhosted.org/packages/bb/73/eda39f7a2d306ff0ffc574afd13c0bbb6d10a603d9a413998ee269487a80/scipy-1.18.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b6f758e35f12757b5d95c00bc6de2438e229c2664b7a92e96f205959d9f2dfa4", size = 37404785, upload-time = "2026-06-19T15:00:34.072Z" }, + { url = "https://files.pythonhosted.org/packages/b7/d2/ae881ee28d014f38e0ccbfd974a06a919ba9af34f1f74bf42b5301891d63/scipy-1.18.0-cp313-cp313-win_amd64.whl", hash = "sha256:1afac4a847207c7ff8efd321734a50b06d0280b3b2a2c0fc2f413101747ad7c7", size = 36554943, upload-time = "2026-06-19T15:00:36.903Z" }, + { url = "https://files.pythonhosted.org/packages/70/3a/21154e2d54eb3639c6bf4dbae2e531c68356bfe95990daa30df33b30d556/scipy-1.18.0-cp313-cp313-win_arm64.whl", hash = "sha256:c5dbddf60e58c2312316d097271a8e73d40eaf2eabfa4d95ed7d3695bbf2ce7b", size = 24350911, upload-time = "2026-06-19T15:00:40.062Z" }, + { url = "https://files.pythonhosted.org/packages/78/b5/915a19b3de2f7430062b509653563db1633ddbb6f021b06731521115d4e2/scipy-1.18.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:4c256ee70c0d1a8a2ace807e199ccd4e3f57037433842abb3fb36bc17eaa9578", size = 31036253, upload-time = "2026-06-19T15:00:43.216Z" }, + { url = "https://files.pythonhosted.org/packages/d7/88/b72def7262e150d16be13fca37a96481138d624e700340bc3362a7588929/scipy-1.18.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:2ef3abc54a4ffc53765374b0d5728532dfdd2585ed23f6b11c206a1f0b1b9af8", size = 28673758, upload-time = "2026-06-19T15:00:46.663Z" }, + { url = "https://files.pythonhosted.org/packages/91/02/2e636a61a525632c373cf6a9c24442a3ffb79e364d38e98b32042964ac32/scipy-1.18.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:f2a6af57bd9e4a75d70e4117e78a1bbee84f79ae3fbb6d0111005d6ebcc4cb8d", size = 20415514, upload-time = "2026-06-19T15:00:49.399Z" }, + { url = "https://files.pythonhosted.org/packages/c9/b6/2135974442f6aba159d9d39d774a1c8cb19947016725d69fecc685df45bf/scipy-1.18.0-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:3f1ac564d3bf6c03d861d2cd87a1bea0da2887136f7fb1bf519c05a8971452d6", size = 23034398, upload-time = "2026-06-19T15:00:51.941Z" }, + { url = "https://files.pythonhosted.org/packages/f6/e6/ba89ec5abf6ee9257c0d1ec985573f3ae32742c24bc03e016388a40b1b15/scipy-1.18.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:40395a5fcd1abee49a5c7aaa98c29db393eedc835138560a588c47ec16156690", size = 33998032, upload-time = "2026-06-19T15:00:54.838Z" }, + { url = "https://files.pythonhosted.org/packages/7f/c4/bc41eb19b0fd0db868f4132920879019318d80cc522ad8f2bca4611af808/scipy-1.18.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8ca01e8ae69f1b18e9a58d91afead31be3cef0dd905a10249dac559ee15460a0", size = 35283333, upload-time = "2026-06-19T15:00:58.152Z" }, + { url = "https://files.pythonhosted.org/packages/53/a4/cbdeef6eb3830a8462a9d4ada814de5fc984345cc9ecf17cbec51a036f1e/scipy-1.18.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7a7f3b01647384dbc3a711e8c6778e0aabbe93959249fef5c7393396bcac0867", size = 35610216, upload-time = "2026-06-19T15:01:01.155Z" }, + { url = "https://files.pythonhosted.org/packages/80/4d/b2b82502b65f661d1b789c1665dcdf315d5f12194e06fc0b37946294ebae/scipy-1.18.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6aa94e78ec192a30063a5e72e561c28af769dc311190b24fe91774eff1969709", size = 37418960, upload-time = "2026-06-19T15:01:04.155Z" }, + { url = "https://files.pythonhosted.org/packages/93/3e/902d836831474b0ab5a37d16404f7bc5fafd9efba632890e271ba952635f/scipy-1.18.0-cp314-cp314-win_amd64.whl", hash = "sha256:2d8bbdc6c817f5b4006a54d799d4f5bab6f910193cbb9a1ff310833d4d270f61", size = 37288845, upload-time = "2026-06-19T15:01:07.822Z" }, + { url = "https://files.pythonhosted.org/packages/b6/43/8d73b337a3bdb14daa0314f0434210747c02d79d729ce1777574a817dcf6/scipy-1.18.0-cp314-cp314-win_arm64.whl", hash = "sha256:18e9575f1569b2c54174e6159d32942e03731177f63dce7975f0a0c88d102f5b", size = 24988971, upload-time = "2026-06-19T15:01:11.076Z" }, + { url = "https://files.pythonhosted.org/packages/b4/b4/f11918b0508a2787031a0499a03fbe3546f3bb5ca05d01038c45b278c09a/scipy-1.18.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:f351e0dd702687d12a402b867a1b4146a256923e1c38317cbc472f6372b94707", size = 31399325, upload-time = "2026-06-19T15:01:13.723Z" }, + { url = "https://files.pythonhosted.org/packages/7b/d1/1f287b57c0ff0ee5185dff3946d92c8017d39b0e431f0ae79a3ff1859512/scipy-1.18.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:7c7a51b33ce387193c97f228320cf8e87361daa1bba750638677729598b3e677", size = 29092110, upload-time = "2026-06-19T15:01:16.908Z" }, + { url = "https://files.pythonhosted.org/packages/ff/1a/7b74eb6c392fdcb27d414c0e7558a6d0231eb3b6d73571f479bb81ea8794/scipy-1.18.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:84031d7b052a54fae2f8632e0ec802073d385476eb9a63079bce6e23ef9283d4", size = 20833811, upload-time = "2026-06-19T15:01:20.488Z" }, + { url = "https://files.pythonhosted.org/packages/7c/ad/f3941716320a7b9cb4d68734a903b45fe16eff5fb7da7e16f2e619304979/scipy-1.18.0-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:56abf29a7c067dde59be8b9a22d606a4ea1b2f2a4b756d9d903c62818f5dacce", size = 23396644, upload-time = "2026-06-19T15:01:23.364Z" }, + { url = "https://files.pythonhosted.org/packages/22/22/1446b62ffe07f9719b7d9b1b6a4e05a772833ae8f441fe4c22c34c9b250f/scipy-1.18.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1ad44305cfa24b1ba5803cbbebf033590ccbac1aa5d612d727b785325ab408b0", size = 34079318, upload-time = "2026-06-19T15:01:26.002Z" }, + { url = "https://files.pythonhosted.org/packages/56/3b/b87da667098bb470fa30c7011b0ba351ee976dd395c78798c66e941665a3/scipy-1.18.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:945c1761b93f38d7f99ae81ae80c63e621471608c7eeead563f6df025585cd58", size = 35324320, upload-time = "2026-06-19T15:01:28.881Z" }, + { url = "https://files.pythonhosted.org/packages/f8/a1/c7932f91909759b0267f75fdea34e91309f96b895757534b76a90b6b4344/scipy-1.18.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:1a4441f15d620578772a49e5ab48c0ee1f7a0220e387110283062729136b2553", size = 35699541, upload-time = "2026-06-19T15:01:31.968Z" }, + { url = "https://files.pythonhosted.org/packages/f7/86/5185061a1fcc41d18c5dc2463969b3a3964b31d9ac67b2fb05d4c7ff7670/scipy-1.18.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9aac6192fac56bf2ca534389d24623f07b39ff83317d58287285e7fbd622ff76", size = 37472480, upload-time = "2026-06-19T15:01:35.136Z" }, + { url = "https://files.pythonhosted.org/packages/31/8e/f04c68e39919a010d34f2ee1367fd705b0a25a02f609d755f0bfbc0a15fc/scipy-1.18.0-cp314-cp314t-win_amd64.whl", hash = "sha256:e40baea28ae7f5475c779741e2d90b1247c78531207b49c7030e698ff81cee3f", size = 37365390, upload-time = "2026-06-19T15:01:38.091Z" }, + { url = "https://files.pythonhosted.org/packages/d5/19/969dc072906c84dd0a3b05dcf57ea750936087d7873549e408b35cfc3f97/scipy-1.18.0-cp314-cp314t-win_arm64.whl", hash = "sha256:368e0a705903c466aa5f08eefb39e6b1b6b2d659e7352a31fd9e2438365be0f8", size = 25279661, upload-time = "2026-06-19T15:01:40.817Z" }, +] + [[package]] name = "seaborn" version = "0.13.2" @@ -435,6 +529,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, ] +[[package]] +name = "threadpoolctl" +version = "3.6.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274, upload-time = "2025-03-13T13:49:23.031Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638, upload-time = "2025-03-13T13:49:21.846Z" }, +] + [[package]] name = "tzdata" version = "2026.2" From 1cfe762db984b964f115e74fa64d8636ce662a5a Mon Sep 17 00:00:00 2001 From: Abhishek Reddy Date: Thu, 16 Jul 2026 08:46:38 +0530 Subject: [PATCH 3/3] minor bugs resolved --- PULL_REQUEST_TEMPLATE.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PULL_REQUEST_TEMPLATE.md b/PULL_REQUEST_TEMPLATE.md index 16ff244..a78d091 100644 --- a/PULL_REQUEST_TEMPLATE.md +++ b/PULL_REQUEST_TEMPLATE.md @@ -76,10 +76,10 @@ List the important hyperparameters used. Attach **at least 2 plots** from your analysis. --- **Confusion Matrix** -![alt text](image.png) +![alt text](confusion_matrix.png) --- **Scatter Plot** -![alt text](image-1.png) +![alt text](anomaly_scatter_plot.png) --- ---