diff --git a/lab1/dataset_plot.png b/lab1/dataset_plot.png
new file mode 100644
index 0000000..17492eb
Binary files /dev/null and b/lab1/dataset_plot.png differ
diff --git a/lab1/preprocess.py b/lab1/preprocess.py
index 1e57e26..0780cc4 100644
--- a/lab1/preprocess.py
+++ b/lab1/preprocess.py
@@ -1,8 +1,160 @@
 import pandas as pd
-
+from sklearn.preprocessing import MinMaxScaler
+import matplotlib.pyplot as plt
 
 input_data = pd.read_csv("annthyroid_unsupervised_anomaly_detection.csv", sep=";")
 
 print(input_data.head(10))
 # deal with missing values
-print(input_data[ input_data.isna().any(axis=1) ])
\ No newline at end of file
+
+# print all rows with missing values
+print("---All rows with missing values---")
+print(input_data[ input_data.isna().any(axis=1) ])
+
+# Check for missing values in each column
+missing_values = input_data.isnull().sum()
+
+# Filter columns with missing values
+columns_with_missing_values = missing_values[missing_values > 0].index
+
+# Print columns with missing values
+print("---Columns with missing values---")
+print(columns_with_missing_values)
+
+print("Filling column 'on_antithyroid_medication' with mode of this column")
+input_data["on_antithyroid_medication"] = input_data["on_antithyroid_medication"].fillna(input_data["on_antithyroid_medication"].mode().iloc[0])
+
+print("Filling column 'pregnant' with mode of this column")
+input_data["pregnant"] = input_data["pregnant"].fillna(input_data["pregnant"].mode().iloc[0])
+
+print("Filling column 'thyroid_surgery' with mode of this column")
+input_data["thyroid_surgery"] = input_data["thyroid_surgery"].fillna(input_data["thyroid_surgery"].mode().iloc[0])
+
+print("Filling column 'query_hypothyroid' with mode of this column")
+input_data["query_hypothyroid"] = input_data["query_hypothyroid"].fillna(input_data["query_hypothyroid"].mode().iloc[0])
+
+print("Filling column 'query_hyperthyroid' with mode of this column")
+input_data["query_hyperthyroid"] = input_data["query_hyperthyroid"].fillna(input_data["query_hyperthyroid"].mode().iloc[0])
+
+print("Filling column 'lithium' with mode of this column")
+input_data["lithium"] = input_data["lithium"].fillna(input_data["lithium"].mode().iloc[0])
+
+print("Filling column 'TSH' with median of this column")
+input_data["TSH"] = input_data["TSH"].fillna(input_data["TSH"].median())
+
+print("Filling column 'T3_measured' with median of this column")
+input_data["T3_measured"] = input_data["T3_measured"].fillna(input_data["T3_measured"].median())
+
+print("Filling column 'TT4_measured' with median of this column")
+input_data["TT4_measured"] = input_data["TT4_measured"].fillna(input_data["TT4_measured"].median())
+
+print("Filling column 'T4U_measured' with median of this column")
+input_data["T4U_measured"] = input_data["T4U_measured"].fillna(input_data["T4U_measured"].median())
+
+print("Filling column 'FTI_measured' with median of this column")
+input_data["FTI_measured"] = input_data["FTI_measured"].fillna(input_data["FTI_measured"].median())
+
+print("---Checking after filling N/A values---")
+# Check for missing values in each column
+missing_values = input_data.isnull().sum()
+
+# Filter columns with missing values
+columns_with_missing_values = missing_values[missing_values > 0].index
+
+# Print columns with missing values
+print("---Columns with missing values (after filling)---")
+print(columns_with_missing_values)
+
+# print all rows with missing values
+print("---Rows with missing values (after filling)---")
+print(input_data[ input_data.isna().any(axis=1) ])
+
+columns_with_continuous_attributes = ["TSH", "T3_measured", "TT4_measured", "T4U_measured", "FTI_measured"]
+
+# normalize columns with continuous attributes
+print("---Normalizing continuous attributes---")
+scaler = MinMaxScaler()
+input_data[columns_with_continuous_attributes] = scaler.fit_transform(input_data[columns_with_continuous_attributes])
+
+# detecting the outliers based on columns with continuous attributes
+Q1 = input_data[columns_with_continuous_attributes].quantile(0.25)
+Q3 = input_data[columns_with_continuous_attributes].quantile(0.75)
+IQR = Q3 - Q1
+lower_bound = Q1 - 2.5 * IQR
+upper_bound = Q3 + 2.5 * IQR
+found_outliers = ( (input_data[columns_with_continuous_attributes] < lower_bound) | (input_data[columns_with_continuous_attributes] > upper_bound) ).any(axis=1)
+print("---Detected outliers based on continuous attributes---")
+print(input_data[found_outliers])
+
+color_mapping = {
+    "o": "red",
+    "n": "green"
+}
+ax = input_data.plot(x="TSH", y="T3_measured", kind="scatter", c=input_data["Outlier_label"].map(color_mapping), colormap="viridis")
+legend_config = [plt.Line2D([0], [0], marker="o", color="w", markerfacecolor=color, markersize=10, label=label) for label, color in color_mapping.items()]
+ax.legend(handles=legend_config, title="Categories: is outlier? \n(o - outlier, n - not an outlier)")
+plt.title('Correlation between TSH, T3 markers \nand outlier classification')
+plt.savefig('tsh_t3_plot.png')
+
+ax = input_data.plot(x="TSH", y="TT4_measured", kind="scatter", c=input_data["Outlier_label"].map(color_mapping), colormap="viridis")
+legend_config = [plt.Line2D([0], [0], marker="o", color="w", markerfacecolor=color, markersize=10, label=label) for label, color in color_mapping.items()]
+ax.legend(handles=legend_config, title="Categories: is outlier? \n(o - outlier, n - not an outlier)")
+plt.title('Correlation between TSH, TT4 markers \nand outlier classification')
+plt.savefig('tsh_tt4_plot.png')
+
+ax = input_data.plot(x="TSH", y="T4U_measured", kind="scatter", c=input_data["Outlier_label"].map(color_mapping), colormap="viridis")
+legend_config = [plt.Line2D([0], [0], marker="o", color="w", markerfacecolor=color, markersize=10, label=label) for label, color in color_mapping.items()]
+ax.legend(handles=legend_config, title="Categories: is outlier? \n(o - outlier, n - not an outlier)")
+plt.title('Correlation between TSH, T4U markers \nand outlier classification')
+plt.savefig('tsh_t4u_plot.png')
+
+ax = input_data.plot(x="TSH", y="FTI_measured", kind="scatter", c=input_data["Outlier_label"].map(color_mapping), colormap="viridis")
+legend_config = [plt.Line2D([0], [0], marker="o", color="w", markerfacecolor=color, markersize=10, label=label) for label, color in color_mapping.items()]
+ax.legend(handles=legend_config, title="Categories: is outlier? \n(o - outlier, n - not an outlier)")
+plt.title('Correlation between TSH, FTI markers \nand outlier classification')
+plt.savefig('tsh_fti_plot.png')
+
+dataset_cleaned = input_data[~found_outliers]
+
+ax = dataset_cleaned.plot(x="TSH", y="T3_measured", kind="scatter", c=dataset_cleaned["Outlier_label"].map(color_mapping), colormap="viridis")
+legend_config = [plt.Line2D([0], [0], marker="o", color="w", markerfacecolor=color, markersize=10, label=label) for label, color in color_mapping.items()]
+ax.legend(handles=legend_config, title="Categories: is outlier? \n(o - outlier, n - not an outlier)")
+plt.title('Correlation between TSH, T3 markers \n and outlier classification (after outliers cleaning)')
+plt.savefig('tsh_t3_plot_outliers_removed.png')
+
+ax = dataset_cleaned.plot(x="TSH", y="TT4_measured", kind="scatter", c=dataset_cleaned["Outlier_label"].map(color_mapping), colormap="viridis")
+legend_config = [plt.Line2D([0], [0], marker="o", color="w", markerfacecolor=color, markersize=10, label=label) for label, color in color_mapping.items()]
+ax.legend(handles=legend_config, title="Categories: is outlier? \n(o - outlier, n - not an outlier)")
+plt.title('Correlation between TSH, TT4 markers \nand outlier classification (after outliers cleaning)')
+plt.savefig('tsh_tt4_plot_outliers_removed.png')
+
+ax = dataset_cleaned.plot(x="TSH", y="T4U_measured", kind="scatter", c=dataset_cleaned["Outlier_label"].map(color_mapping), colormap="viridis")
+legend_config = [plt.Line2D([0], [0], marker="o", color="w", markerfacecolor=color, markersize=10, label=label) for label, color in color_mapping.items()]
+ax.legend(handles=legend_config, title="Categories: is outlier? \n(o - outlier, n - not an outlier)")
+plt.title('Correlation between TSH, T4U markers \nand outlier classification (after outliers cleaning)')
+plt.savefig('tsh_t4u_plot_outliers_removed.png')
+
+ax = dataset_cleaned.plot(x="TSH", y="FTI_measured", kind="scatter", c=dataset_cleaned["Outlier_label"].map(color_mapping), colormap="viridis")
+legend_config = [plt.Line2D([0], [0], marker="o", color="w", markerfacecolor=color, markersize=10, label=label) for label, color in color_mapping.items()]
+ax.legend(handles=legend_config, title="Categories: is outlier? \n(o - outlier, n - not an outlier)")
+plt.title('Correlation between TSH, FTI markers \nand outlier classification (after outliers cleaning)')
+plt.savefig('tsh_fti_plot_outliers_removed.png')
+
+# rate the outlier detection based on the Outlier_label column
+false_positives_count = input_data[(found_outliers) & (input_data["Outlier_label"] == "n")]["Outlier_label"].count()
+false_negatives_count = dataset_cleaned[dataset_cleaned["Outlier_label"] == "o"]["Outlier_label"].count()
+true_postitives_count = input_data[(found_outliers) & (input_data["Outlier_label"] == "o")]["Outlier_label"].count()
+true_negatives_count = dataset_cleaned[dataset_cleaned["Outlier_label"] == "n"]["Outlier_label"].count()
+precision = true_negatives_count/(true_negatives_count + false_negatives_count)
+recall = true_negatives_count/(true_negatives_count + false_positives_count)
+
+print(f"""
+---Classification of the outlier detection---
+(we treat negative prediction as our target) 
+True positives: {true_postitives_count}
+False positives: {false_positives_count}
+True negatives: {true_negatives_count}
+False negatives: {false_negatives_count}
+Precision: {precision}
+Recall: {recall}
+""")
\ No newline at end of file
diff --git a/lab1/requirements.txt b/lab1/requirements.txt
index 02317a7..e1f36f0 100644
--- a/lab1/requirements.txt
+++ b/lab1/requirements.txt
@@ -1,6 +1,19 @@
+contourpy==1.2.0
+cycler==0.12.1
+fonttools==4.45.0
+joblib==1.3.2
+kiwisolver==1.4.5
+matplotlib==3.8.2
 numpy==1.26.2
+packaging==23.2
 pandas==2.1.3
+Pillow==10.1.0
+pyparsing==3.1.1
 python-dateutil==2.8.2
 pytz==2023.3.post1
+scikit-learn==1.3.2
+scipy==1.11.4
 six==1.16.0
+sklearn==0.0.post11
+threadpoolctl==3.2.0
 tzdata==2023.3
diff --git a/lab1/tsh_fti_plot.png b/lab1/tsh_fti_plot.png
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diff --git a/lab1/tsh_fti_plot_outliers_removed.png b/lab1/tsh_fti_plot_outliers_removed.png
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index 0000000..076fe4e
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diff --git a/lab1/tsh_t3_plot.png b/lab1/tsh_t3_plot.png
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diff --git a/lab1/tsh_t3_plot_outliers_removed.png b/lab1/tsh_t3_plot_outliers_removed.png
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index 0000000..ee7c071
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diff --git a/lab1/tsh_t4u_plot.png b/lab1/tsh_t4u_plot.png
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index 0000000..7b916be
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diff --git a/lab1/tsh_t4u_plot_outliers_removed.png b/lab1/tsh_t4u_plot_outliers_removed.png
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index 0000000..9529b78
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diff --git a/lab1/tsh_tt4_plot.png b/lab1/tsh_tt4_plot.png
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diff --git a/lab1/tsh_tt4_plot_outliers_removed.png b/lab1/tsh_tt4_plot_outliers_removed.png
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index 0000000..a96b04a
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