Update pages/3_Life Cycle Of ML Project.py
Browse files
pages/3_Life Cycle Of ML Project.py
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@@ -40,16 +40,17 @@ elif st.session_state.page == "data_collection":
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In the context of Machine Learning (ML), data plays an even more pivotal role. It serves as the input for algorithms, enabling them to learn, predict, and classify based on patterns found in the data. Without data, there is nothing for the ML model to learn from, and consequently, the model cannot make any predictions or inferences.
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""")
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st.markdown(""":blue[Types of Data]:
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\nStructured
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\nUnstructured
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\nSemi-Structured""")
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# Button for Structured Data
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if st.button(":blue[π Structured Data]"):
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st.markdown("### Structured Data\nStructured data is highly organized and typically stored in tables like spreadsheets or databases. It is easy to search and analyze.")
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st.markdown("Examples: Excel files, CSV files")
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# Button for Unstructured Data
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if st.button(":blue[π Unstructured Data]"):
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In the context of Machine Learning (ML), data plays an even more pivotal role. It serves as the input for algorithms, enabling them to learn, predict, and classify based on patterns found in the data. Without data, there is nothing for the ML model to learn from, and consequently, the model cannot make any predictions or inferences.
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""")
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st.markdown(""":blue[Types of Data]:
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Data is divided into Three Types.
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\nStructured
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\nUnstructured
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\nSemi-Structured""")
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# Button for Structured Data
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if st.button(":blue[π Structured Data]"):
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st.session_state.page = "Structured Data"
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st.markdown("### Structured Data\nStructured data is highly organized and typically stored in tables like spreadsheets or databases. It is easy to search and analyze.")
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st.markdown("Examples: Excel files, CSV files")
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# Button for Unstructured Data
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if st.button(":blue[π Unstructured Data]"):
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