diff --git a/.gitignore b/.gitignore
index d1085d15d..8c4c6e9e8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -47,6 +47,8 @@ original/
 
 # possible output file
 *.csv
+!pyincore/analyses/*/*.csv
+!/tests/pyincore/analyses/*/*.csv
 # a pair of negation pattern; everything else except below will be ignored by git
 *.json
 !tests/data/*/*.json
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2e1fd5d93..292efe748 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,9 +8,13 @@ and this project adheres to [Semantic Versioning](http://semver.org/).
 
 ## [1.18.0] - 2024-04-03
 
+### Added
+- Add CoreCGEML module [#468](https://github.com/IN-CORE/pyincore/issues/468)
+- Add ML enabled SLC CGE [#508](https://github.com/IN-CORE/pyincore/issues/508)
+
 ### Changed
-- Building Functionality column renaming [#510](https://github.com/IN-CORE/pyincore/issues/510)
 - Need to use left join so the building number stays the same [#514](https://github.com/IN-CORE/pyincore/issues/541)
+- Building Functionality column renaming [#510](https://github.com/IN-CORE/pyincore/issues/510)
 - Update the post-processing step for generating population dislocation map layer [#526](https://github.com/IN-CORE/pyincore/issues/526)
 
 
diff --git a/pyincore/analyses/core_cge_ml/__init__.py b/pyincore/analyses/core_cge_ml/__init__.py
new file mode 100644
index 000000000..3c5fba791
--- /dev/null
+++ b/pyincore/analyses/core_cge_ml/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2024 University of Illinois and others. All rights reserved.
+#
+# This program and the accompanying materials are made available under the
+# terms of the Mozilla Public License v2.0 which accompanies this distribution,
+# and is available at https://www.mozilla.org/en-US/MPL/2.0/
+
+from pyincore.analyses.core_cge_ml.corecgeml import CoreCGEML
\ No newline at end of file
diff --git a/pyincore/analyses/core_cge_ml/corecgeml.py b/pyincore/analyses/core_cge_ml/corecgeml.py
new file mode 100644
index 000000000..487f8dc3f
--- /dev/null
+++ b/pyincore/analyses/core_cge_ml/corecgeml.py
@@ -0,0 +1,267 @@
+# Copyright (c) 2024 University of Illinois and others. All rights reserved.
+#
+# This program and the accompanying materials are made available under the
+# terms of the Mozilla Public License v2.0 which accompanies this distribution,
+# and is available at https://www.mozilla.org/en-US/MPL/2.0/
+
+from typing import List, Dict, Optional
+from collections import defaultdict
+import numpy as np
+import pandas as pd
+
+from pyincore import BaseAnalysis
+
+
+class CoreCGEML(BaseAnalysis):
+    """Core CGE ML"""
+
+    def __init__(
+        self,
+        incore_client,
+        sectors: Dict[str, List[str]],
+        labor_groups: Optional[List[str]] = None,
+    ):
+        super(CoreCGEML, self).__init__(incore_client)
+
+        self.factors = [
+            "domestic_supply",
+            "gross_income",
+            "household_count",
+            "factor_demand",
+        ]
+        self.sectors = sectors
+        self.labor_groups = ["L1", "L2", "L3"] if labor_groups is None else labor_groups
+
+    def construct_output(self, predictions: dict) -> Dict[str, Dict[str, list]]:
+        """construct_output will construct the output in the format required by the pyincore.
+        The output will be a tuple of 5 elements. Each element will be a dictionary according to the following format:
+        - Domestic Supply:
+            Sectors: list of domestic supply sectors
+            DS0: domestic supply values before disaster
+            DSL: domestic supply values after disaster
+        - Gross Income:
+            Household Group: list of houshold sectors
+            Y0: Household income before disaster
+            YL: Household income after disaster
+        - Household Count:
+            Household Group: list of houshold sectors
+            HH0: Household count before disaster
+            HHL: Household count after disaster
+        - Pre-Disaster Factor Demand:
+            Labor Group: list of labor groups
+            sector1:
+            sector2:
+            .
+            .
+            .
+            sectorN:
+        - Post-Disaster Factor Demand:
+            Labor Group: list of labor groups
+            sector1:
+            sector2:
+            .
+            .
+            .
+            sectorN:
+
+        Parameters
+        ----------
+        predictions : dict
+            This is a dictionary with keys as factors and values as a dictionary with keys "before" and "after".
+
+        Returns
+        -------
+        Dict[str, Dict[str, list]]
+            A dictionary of 5 dictionaries.
+        """
+        constructed_outputs = {}
+        if predictions.get("ds", None) is not None:
+            constructed_outputs["ds"] = {
+                "Sectors": self.sectors["ds"],
+                "DS0": predictions["ds"]["before"],
+                "DSL": predictions["ds"]["after"],
+            }
+        if predictions.get("dy", None) is not None:
+            constructed_outputs["dy"] = {
+                "Household Group": self.sectors["dy"],
+                "Y0": predictions["dy"]["before"],
+                "YL": predictions["dy"]["after"],
+            }
+        if predictions.get("migt", None) is not None:
+            constructed_outputs["migt"] = {
+                "Household Group": self.sectors["migt"],
+                "HH0": predictions["migt"]["before"],
+                "HHL": predictions["migt"]["after"],
+            }
+        if predictions.get("dffd", None) is not None:
+            prefd = {
+                "Labor Group": self.labor_groups,
+            }
+            postfd = {
+                "Labor Group": self.labor_groups,
+            }
+
+            temp_prefd: Dict[str, Dict[str, float]] = defaultdict(dict)
+            temp_postfd: Dict[str, Dict[str, float]] = defaultdict(dict)
+            for i, fd_sector in enumerate(self.sectors["dffd"]):
+                splits = fd_sector.split("_")
+                if len(splits) > 2:
+                    sector = "_".join(splits[:-1])
+                    grp = splits[-1]
+
+                temp_prefd[sector][grp] = predictions["dffd"]["before"][i]
+                temp_postfd[sector][grp] = predictions["dffd"]["after"][i]
+
+            for sector in temp_prefd.keys():
+                prefd_l = []
+                postfd_l = []
+                for grp in self.labor_groups:
+
+                    if temp_prefd[sector].get(grp, None) is None:
+                        prefd_l.append(-1)
+                    else:
+                        prefd_l.append(temp_prefd[sector][grp])
+
+                    if temp_postfd[sector].get(grp, None) is None:
+                        postfd_l.append(-1)
+                    else:
+                        postfd_l.append(temp_postfd[sector][grp])
+
+                prefd[sector] = prefd_l
+                postfd[sector] = postfd_l
+
+            constructed_outputs["prefd"] = prefd
+            constructed_outputs["postfd"] = postfd
+
+        return constructed_outputs
+
+    def run_core_cge_ml(
+        self,
+        base_cap: np.ndarray,
+        capital_shocks: np.ndarray,
+        model_coeffs: Dict[str, np.ndarray],
+        base_cap_factors: List[np.ndarray],
+    ) -> None:
+        """run_core_cge_ml will use the model coefficients to predict the change in capital stock for each sector.
+        The predicted change will then be added to base_cap_factors to get the final capital stock for each sector 
+        after a disaster.
+        The model requires capital stock loss in dollar amount, hence the base_cap will be used to 
+        calculate the loss in dollar amount.
+        The capital_shocks is the percentage of capital stock that remains and hence to get the loss we 
+        use 1 - capital_shocks.
+
+        Some variables for parameters:
+        - n: number of factors
+        - k_i: number of sectors (including subsectors) for factor i.
+        - K: number of sectors (including subsectors) for input to model.
+        - l_i: number of coefficients for a model for factor i.
+
+        The length of model_coeffs == length of base_cap_factors == n.
+
+        #TODO: Add detail about the length of base_cap being equal to K and l_i. Meaning K == l_i for i = 1, 2, ..., n.
+
+        Parameters
+        ----------
+        base_cap : (1 X K) np.ndarray
+           This is the base capital for each sector in dollar amount in Millions. This is a (1, K) array with K elements.
+            The shape should match the shape of capital_shocks.
+        capital_shocks : (1 X K) np.ndarray
+            This is the capital shock for each sector in percentage. This is a (1, K) array with K elements.
+        model_coeffs : Dict[str, np.ndarray]
+            This is a dictionary of 2D arrays with shape [n, (k_i, l_i)]. 
+            Each entry in the dictionary corresponds to a factor and each factor has k_i number of models.
+            It is assumed that the intercept term is included in the model coefficients and is at the 0th column.
+        base_cap_factors : List[np.ndarray]
+            This is a list of 1D array with each entry corresponding to a factor representing its base capital by sectors.
+            This is the base capital for each sector for a given factor.
+            The list would look like [(m_1, 1), (m_2, 1), ..., (m_n, 1)].
+        """
+
+        # check if the shape of base_cap and capital_shocks match
+        if base_cap.shape != capital_shocks.shape:
+            raise ValueError(
+                "The shape of base_cap and capital_shocks do not match. Base Cap shape {}, Capital Shocks shape {}".format(
+                    base_cap.shape, capital_shocks.shape
+                )
+            )
+
+        # Convert capital_shocks to capital percent loss and then to capital loss in dollar amount
+        capital_loss = (1 - capital_shocks) * base_cap
+
+        # add a bias term to the capital loss with value 1 resulting in a shape of (1, 1+K)
+        capital_loss = np.hstack((np.ones((1, 1)), capital_loss))
+
+        assert len(model_coeffs) == len(
+            base_cap_factors
+        ), "The length of model_coeffs and base_cap_factors do not match. required length {}, observed length {}".format(
+            len(model_coeffs), len(base_cap_factors)
+        )
+
+        predictions = {}
+        for factor_base_cap_before, (factor, factor_model_coeff) in zip(
+            base_cap_factors, model_coeffs.items()
+        ):
+            # multiply the capital loss with the model coefficients (k_i x 1) = (1, 1+K) . (k_i, l_i)
+            factor_capital_loss: np.ndarray = capital_loss.dot(factor_model_coeff.T).T
+
+            assert (
+                factor_capital_loss.shape == factor_base_cap_before.shape
+            ), "Number of sectors in models and base_cap_factors do not match. required shape {}, observed shape {}".format(
+                factor_capital_loss.shape, factor_base_cap_before.shape
+            )
+            # add the predicted change in capital stock to the base_cap_factors
+            factor_base_cap_after: np.ndarray = (
+                factor_base_cap_before + factor_capital_loss
+            )
+
+            predictions[factor] = {
+                "before": np.squeeze(factor_base_cap_before).tolist(),
+                "after": np.squeeze(factor_base_cap_after).tolist(),
+            }
+
+        constructed_outputs = self.construct_output(predictions)
+
+        file_prefix = (
+            self.get_parameter("result_name")
+            if self.get_parameter("result_name") is not None
+            else ""
+        )
+        file_prefix += "_"
+
+        if constructed_outputs.get("ds", None) is not None:
+            self.set_result_csv_data(
+                "domestic-supply",
+                pd.DataFrame(constructed_outputs["ds"]),
+                name=f"{file_prefix}domestic-supply",
+                source="dataframe",
+            )
+        if constructed_outputs.get("dy", None) is not None:
+            self.set_result_csv_data(
+                "gross-income",
+                pd.DataFrame(constructed_outputs["dy"]),
+                name=f"{file_prefix}gross-income",
+                source="dataframe",
+            )
+        if constructed_outputs.get("migt", None) is not None:
+            self.set_result_csv_data(
+                "household-count",
+                pd.DataFrame(constructed_outputs["migt"]),
+                name=f"{file_prefix}household-count",
+                source="dataframe",
+            )
+        if constructed_outputs.get("prefd", None) is not None:
+            self.set_result_csv_data(
+                "pre-disaster-factor-demand",
+                pd.DataFrame(constructed_outputs["prefd"]),
+                name=f"{file_prefix}pre-disaster-factor-demand",
+                source="dataframe",
+            )
+        if constructed_outputs.get("postfd", None) is not None:
+            self.set_result_csv_data(
+                "post-disaster-factor-demand",
+                pd.DataFrame(constructed_outputs["postfd"]),
+                name=f"{file_prefix}post-disaster-factor-demand",
+                source="dataframe",
+            )
+
+        return True
diff --git a/pyincore/analyses/mlenabledcgeslc/DDS_coefficients.csv b/pyincore/analyses/mlenabledcgeslc/DDS_coefficients.csv
new file mode 100644
index 000000000..37950f8f1
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/DDS_coefficients.csv
@@ -0,0 +1,79 @@
+Testbed,Hazard,Model_Type,Model_Name,(Intercept),ART_ACC_R1,ART_ACC_R2,ART_ACC_R3,ART_ACC_R4,ART_ACC_R5,ART_ACC_R6,ART_ACC_R7,COMMER_R1,COMMER_R2,COMMER_R3,COMMER_R4,COMMER_R5,COMMER_R6,COMMER_R7,CONS_R1,CONS_R2,CONS_R3,CONS_R4,CONS_R5,CONS_R6,CONS_R7,EDU_R1,EDU_R2,EDU_R3,EDU_R4,EDU_R5,EDU_R6,EDU_R7,HS1_R1,HS1_R2,HS1_R3,HS1_R4,HS1_R5,HS1_R6,HS1_R7,HS2_R1,HS2_R2,HS2_R3,HS2_R4,HS2_R5,HS2_R6,HS2_R7,HEALTH_R1,HEALTH_R2,HEALTH_R3,HEALTH_R4,HEALTH_R5,HEALTH_R6,HEALTH_R7,MANU_R1,MANU_R2,MANU_R3,MANU_R4,MANU_R5,MANU_R6,MANU_R7,RELIG_R1,RELIG_R2,RELIG_R3,RELIG_R4,RELIG_R5,RELIG_R6,RELIG_R7,UTIL_R1,UTIL_R2,UTIL_R3,UTIL_R4,UTIL_R5,UTIL_R6,UTIL_R7,AG_MI_R1,AG_MI_R2,AG_MI_R6,AG_MI_R7
+SLC,earthquake,main_model,totDDS,94.7047615674173,-0.287883918408431,-0.287552740299594,-0.265603552951034,-0.270426296903925,-0.251441335779809,-0.161053875597043,0.0,-0.279906684122777,-0.278294826893055,-0.248057808193457,-0.252279283315536,-0.244050850277802,-0.239123537120185,-0.257225308573057,-0.840216991311522,-0.163923515573549,-0.273974115602849,0.0,0.0,0.0,0.0,0.0,-0.234141415424839,-0.035411427538892,-0.211278137592845,0.0,-0.0554529692830807,0.0,-0.426973108141082,-0.411642214470317,-0.36564602921107,-0.353496706024138,-0.295690617302096,-0.378819835272695,-0.549150075267567,-0.416412237335229,-0.413258820407578,-0.360576429034327,-0.348094506044403,-0.290410250108047,-0.380819188459347,-0.530257770619855,-0.353947494806686,-0.27511045332759,-0.225903999886922,-0.227821749555162,-0.238088480897042,-0.212711777025434,-0.218107181875932,-0.294327783463299,-1.5279495457582,-0.233125310646363,0.0,-0.244884125479214,-0.124930092049231,-0.77222376152391,-0.176233369973929,-0.18408582010479,-0.157656743338269,-0.162229151478494,-0.153482019048179,0.132397126645898,-0.0819930551595193,-0.338757640235611,-0.32771018780876,-0.309468516114291,-0.169771849221317,-0.21346047419723,-0.329639309456361,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,DDS_ ART_ACC_R1,0.628657105312516,-0.248652006868071,0.0,0.0,0.0,0.0,0.0,-0.0028497068529578,-0.0003076180981205,-9.14155281333016e-06,-2.05766319659795e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0006956860963851,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0035680374082481,-0.0010010539988471,-0.0001572769451488,-0.0001628204889186,-0.0001179136137465,-6.76191815564304e-05,-0.0001076108728057,-0.0001763678953369,-0.000482062128356,-0.0001309246063528,-0.0001522782381492,-7.72713966245355e-05,-0.0001465428346204,0.0,-6.52098733421209e-06,0.0050910248641325,0.0,0.0,0.0,0.0,0.0,5.67922006587713e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0002792907414687,0.0,0.0,0.0,0.0,0.0,-0.0074503171432256,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,DDS_ ART_ACC_R2,6.6550556179438,-0.0013388903585957,-0.254430976749413,-0.0003859002347165,0.0,0.0,0.0,0.0179719255538056,0.0,-0.002120530843831,-0.0002582879788583,0.0,0.0,-0.0009543437382477,0.0,0.0,0.0491527763566973,0.0,0.0169300062332138,0.069335488629863,0.0,0.0559155360683112,0.0,0.0007392126568062,0.0,0.0,-0.0114862272486676,-0.0085443849252828,-0.0503880451147692,-0.0023862541609632,-0.0048035517751125,-0.0011731094964121,-0.0014709138828223,-0.0007009945764097,-0.0008774733838592,-0.0018820965608043,0.0,-0.0049401542311252,-0.0017886920451773,-0.0016387013124833,0.0,-0.0007423651772053,-0.0027897705191873,0.0057092857704693,0.0010314543526557,0.0,-0.000356097503437,-0.0010888125353352,-0.0021803484740749,0.0,0.0,0.0,0.0,0.0,0.0,0.0061550990530839,0.0,-0.0012070125056005,0.0021230890066656,-0.0005923371271994,0.0,0.0,0.0,0.0,-0.004427452116001,-0.0051893737697133,0.0,0.0,0.0,-0.0052732359329931,0.0,0.0,0.0,0.0,0.0
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diff --git a/pyincore/analyses/mlenabledcgeslc/DFFD_coefficients.csv b/pyincore/analyses/mlenabledcgeslc/DFFD_coefficients.csv
new file mode 100644
index 000000000..772de63ec
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/DFFD_coefficients.csv
@@ -0,0 +1,229 @@
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diff --git a/pyincore/analyses/mlenabledcgeslc/DS_base_val.csv b/pyincore/analyses/mlenabledcgeslc/DS_base_val.csv
new file mode 100644
index 000000000..3ac94d4e1
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/DS_base_val.csv
@@ -0,0 +1,78 @@
+,sector,value
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diff --git a/pyincore/analyses/mlenabledcgeslc/DY_coefficients.csv b/pyincore/analyses/mlenabledcgeslc/DY_coefficients.csv
new file mode 100644
index 000000000..8a06758fd
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/DY_coefficients.csv
@@ -0,0 +1,30 @@
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diff --git a/pyincore/analyses/mlenabledcgeslc/FD_base_val.csv b/pyincore/analyses/mlenabledcgeslc/FD_base_val.csv
new file mode 100644
index 000000000..499b45164
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/FD_base_val.csv
@@ -0,0 +1,228 @@
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diff --git a/pyincore/analyses/mlenabledcgeslc/GI_base_val.csv b/pyincore/analyses/mlenabledcgeslc/GI_base_val.csv
new file mode 100644
index 000000000..1e9108fd5
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/GI_base_val.csv
@@ -0,0 +1,29 @@
+,household,value
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diff --git a/pyincore/analyses/mlenabledcgeslc/HH_base_val.csv b/pyincore/analyses/mlenabledcgeslc/HH_base_val.csv
new file mode 100644
index 000000000..b3f8967a8
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/HH_base_val.csv
@@ -0,0 +1,29 @@
+,household,value
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diff --git a/pyincore/analyses/mlenabledcgeslc/MIGT_coefficients.csv b/pyincore/analyses/mlenabledcgeslc/MIGT_coefficients.csv
new file mode 100644
index 000000000..c016c4413
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/MIGT_coefficients.csv
@@ -0,0 +1,30 @@
+Testbed,Hazard,Model_Type,Model_Name,(Intercept),ART_ACC_R1,ART_ACC_R2,ART_ACC_R3,ART_ACC_R4,ART_ACC_R5,ART_ACC_R6,ART_ACC_R7,COMMER_R1,COMMER_R2,COMMER_R3,COMMER_R4,COMMER_R5,COMMER_R6,COMMER_R7,CONS_R1,CONS_R2,CONS_R3,CONS_R4,CONS_R5,CONS_R6,CONS_R7,EDU_R1,EDU_R2,EDU_R3,EDU_R4,EDU_R5,EDU_R6,EDU_R7,HS1_R1,HS1_R2,HS1_R3,HS1_R4,HS1_R5,HS1_R6,HS1_R7,HS2_R1,HS2_R2,HS2_R3,HS2_R4,HS2_R5,HS2_R6,HS2_R7,HEALTH_R1,HEALTH_R2,HEALTH_R3,HEALTH_R4,HEALTH_R5,HEALTH_R6,HEALTH_R7,MANU_R1,MANU_R2,MANU_R3,MANU_R4,MANU_R5,MANU_R6,MANU_R7,RELIG_R1,RELIG_R2,RELIG_R3,RELIG_R4,RELIG_R5,RELIG_R6,RELIG_R7,UTIL_R1,UTIL_R2,UTIL_R3,UTIL_R4,UTIL_R5,UTIL_R6,UTIL_R7,AG_MI_R1,AG_MI_R2,AG_MI_R6,AG_MI_R7
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+SLC,earthquake,sectorRegion_model,MIGT_ HH4_R1,0.15814311350681,-0.0179805921329727,0.0,0.0,0.0,0.0,0.0,0.0,-0.0191341363603158,-0.0008062642574019,-0.0010548324017222,-0.0001459231486292,-0.0001893331831959,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,-0.16668182694519,0.0007855512575153,0.0001462250488975,0.0010944208398179,0.000652114373737,0.0010950555487615,0.0035661143765119,-0.162479360990284,0.000318424376553,0.0,0.0003320699770483,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,-0.0113872245733785,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+SLC,earthquake,sectorRegion_model,MIGT_ HH1_R6,-0.187112004859232,0.0,0.0,0.0,0.0,0.0,-0.0010848802622892,0.0,5.20381149428334e-07,0.0,-9.78307543885384e-06,0.0,0.0,-0.0021763437414246,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,-5.59243411427528e-05,-9.74772259761352e-05,-7.3121974084232e-05,-8.84857432499077e-05,-6.96555510397443e-05,-0.0089214027038583,-0.0003764962297452,0.0,-9.73388868385563e-05,-2.94586959948e-05,-5.23493889594981e-05,0.0,-0.0088398777141812,-0.000113297330031,0.0,0.0,0.0,0.0,0.0,-0.0018244243489031,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0020543268197591,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH2_R6,0.0423896640347969,0.0,-5.91714327594499e-05,0.0,0.0,0.0,0.0,0.0,6.23512805063204e-06,-0.0001826011756693,-0.0003365655067954,-0.0001004200710018,-0.0002730931933722,-0.0053998554964622,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0008645433516885,0.0001750314054778,0.0001801299401754,0.0002263961042797,7.21739740891956e-06,-0.0328708195813128,0.0002308504878051,2.82680592103454e-05,5.8437182798912e-05,8.16835449917485e-05,7.87357543138041e-05,0.0,-0.0323935722863662,0.0,0.0,0.0,0.0,0.0,0.0,-0.0028109013503908,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH3_R6,-1.33345884971887,0.0,-0.0003740121544017,0.0,0.0,0.0,0.0,0.0,-0.0011346474151471,-0.001011262542371,-0.0012373674941015,-0.0008288097138655,-0.0009931634577535,-0.0225697999378102,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,-0.0002608558204457,0.0,0.0,0.0,0.0,0.0,0.0022497824450868,0.0012791557595603,0.0010858117922871,0.0013667375701181,0.0005094547031313,-0.0825785995723471,0.0029324267329756,0.0,0.0008329193202104,0.0005167551378305,0.0006900496690963,0.0,-0.0804037019306093,0.0,0.0,0.0,0.0,-0.0002351662158727,0.0,-0.0124926883637315,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH4_R6,-2.97476744367756,0.0,-0.0004199026015241,0.0,0.0,0.0,0.0,0.0,-0.0017432219197592,-0.0022472183689362,-0.0021879691788396,-0.0012070814499895,-0.002695968491171,-0.0303547458092377,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,-0.0009080774808908,0.0,0.0,0.0,0.0,0.0,0.0047037715757984,0.0037879215923505,0.0017292372205751,0.0040517563417966,0.001886801922984,-0.240787261229982,0.0127820885975394,0.0,0.0028214557585418,0.0008785109175938,0.0029788720623872,0.000885318468417,-0.236910300782853,0.0034334753486644,0.0,0.0,0.0,-0.0008352657544332,0.0,-0.014163942648932,0.0,0.0,0.0,0.0,0,-0.0019674094697072,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH1_R7,-0.151555036878537,0.0,-2.34794457571882e-06,0.0,0.0,0.0,0.0,0.0,1.58370005435141e-05,-1.20761744526269e-05,-2.88136392228462e-05,0.0,-8.58585535802263e-06,-4.67040230374116e-07,-0.000283247933995,0.0,0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,8.78134006022735e-05,1.57670886614132e-05,1.97441363924653e-05,2.4785301238693e-05,8.4418693745114e-06,9.10618779825444e-06,-0.0014902823600288,0.0,8.78628032359097e-06,0.0,2.08373612969427e-06,0.0,0.0,-0.0012957300820957,0.0,0.0,0.0,0.0,0.0,0.0,-5.26999663714823e-05,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH2_R7,0.393468835396561,0.0,-0.0001545991749821,0.0,0.0,0.0,0.0,0.0,0.0,-0.0002287877806664,-0.0004797763026328,-5.50435522111939e-05,-0.0002732101297018,-0.0001246594783043,-0.0091943979053052,0.0,0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0012938212724083,0.0004158361988126,0.0003875986189319,0.0005090663754705,0.0001997962166577,0.0002810115038342,-0.0609986954102239,0.0002865775251311,0.0003044868240992,0.0001770633156825,0.0002607883557915,0.0,0.0,-0.0595851689777894,0.0,0.0,0.0,0.0,0.0,0.0,-0.0029075267159597,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH3_R7,-0.0251567658236738,-0.00049601848151,-0.0004744640441426,0.0,0.0,0.0,0.0,-0.0019452133002414,-0.0005317004359404,-0.0005978349188776,-0.0008158934638148,-0.0004929762435147,-0.0006030108126657,-0.0006515295510052,-0.021419058541126,0.0,0,0.0,0.0,0.0,0.0,0.0,0,-0.0004094917040544,0.0,0.0,0.0,0.0,0.0,0.0019536057146488,0.0010599758664647,0.0009031274438388,0.0011258868931706,0.0005778907645587,0.0008382610612094,-0.0684787158481283,0.0010032377681629,0.0009470428230441,0.0007193597969334,0.0008985300320522,0.0003046801563908,0.0004630744103764,-0.066979401255509,0.0,-2.14421443242964e-05,0.0,-0.0004866259337311,-0.0002387337100583,-0.0009513682641018,-0.0116582767233226,-0.0003207375059647,0.0,0.0,0,-0.0003853623073465,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH4_R7,-42.3611296931534,-0.009417292314701,-0.0024511921465049,0.0,0.0,0.0,-0.0110923067203903,-0.155612762034721,-0.0109608070606787,-0.006206064638891,-0.0056242634422349,-0.0088186930717186,-0.0051109427138085,-0.0099009022146417,-0.118152903489619,0.0876274037678235,0,-0.0098296133621306,0.0,-1.00016117683496,-0.0588466151117679,0.628605412585162,0,-0.007364261967017,0.0,0.0,0.0,-0.0505267183115559,0.519934001656624,0.012499838588256,0.0115303826136568,0.0083865966814084,0.0102735694218945,0.0062657048486255,0.0101673337340841,-0.845574958949119,0.00987181376906,0.0119910191329753,0.0073893955302256,0.0109430189035921,0.0077368725676862,0.005811612494201,-0.843882439484597,0.0,0.0,-0.0042469982750497,-0.0041162398552107,-0.0057574132036179,-0.0094830978502682,-0.0716865063652252,-0.0148812659833063,0.0,-0.0083679925853788,0,-0.0048695809392189,0.0,0.0,-0.011300394791737,-0.0068505983125122,-0.0026091905205557,0.0,-0.0084967219276929,0.0,-0.11028874754375,0.0,0.0,0.0,-0.0223302445128593,0.0,0.0,-0.708175703196676,0.0,0.0,0.0,0.0
diff --git a/pyincore/analyses/mlenabledcgeslc/__init__.py b/pyincore/analyses/mlenabledcgeslc/__init__.py
new file mode 100644
index 000000000..b251e805f
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2024 University of Illinois and others. All rights reserved.
+#
+# This program and the accompanying materials are made available under the
+# terms of the Mozilla Public License v2.0 which accompanies this distribution,
+# and is available at https://www.mozilla.org/en-US/MPL/2.0/
+
+from pyincore.analyses.mlenabledcgeslc.mlcgeslc import MlEnabledCgeSlc
\ No newline at end of file
diff --git a/pyincore/analyses/mlenabledcgeslc/baseKAP.csv b/pyincore/analyses/mlenabledcgeslc/baseKAP.csv
new file mode 100644
index 000000000..69fe1c1b6
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/baseKAP.csv
@@ -0,0 +1,75 @@
+,sector,baseKAP
+0,AG_MI_R1,1.32343
+1,UTIL_R1,86.60789
+2,CONS_R1,6.44545
+3,MANU_R1,147.3874
+4,COMMER_R1,1413.324
+5,EDU_R1,3.195087
+6,HEALTH_R1,26.30652
+7,ART_ACC_R1,214.9359
+8,RELIG_R1,136.7312
+9,AG_MI_R2,0.226855
+10,UTIL_R2,163.0497
+11,CONS_R2,13.16167
+12,MANU_R2,1.96456
+13,COMMER_R2,4347.564
+14,EDU_R2,1077.981
+15,HEALTH_R2,267.174
+16,ART_ACC_R2,1170.836
+17,RELIG_R2,286.8778
+18,UTIL_R3,100.4826
+19,CONS_R3,104.0633
+20,MANU_R3,135.9967
+21,COMMER_R3,6324.506
+22,EDU_R3,28.54747
+23,HEALTH_R3,207.7495
+24,ART_ACC_R3,269.0348
+25,RELIG_R3,270.366
+26,UTIL_R4,17.64995
+27,CONS_R4,2.718651
+28,MANU_R4,2.387753
+29,COMMER_R4,1514.915
+30,EDU_R4,15.98366
+31,HEALTH_R4,777.8064
+32,ART_ACC_R4,165.8875
+33,RELIG_R4,45.82616
+34,UTIL_R5,40.22423
+35,CONS_R5,0.732834
+36,MANU_R5,706.6362
+37,COMMER_R5,3101.648
+38,EDU_R5,36.60337
+39,HEALTH_R5,419.9681
+40,ART_ACC_R5,501.4994
+41,RELIG_R5,86.28852
+42,AG_MI_R6,0.671073
+43,UTIL_R6,28.3906
+44,CONS_R6,5.839408
+45,MANU_R6,40.35103
+46,COMMER_R6,1224.491
+47,EDU_R6,18.75531
+48,HEALTH_R6,286.8313
+49,ART_ACC_R6,52.39905
+50,RELIG_R6,8.885036
+51,AG_MI_R7,0.085555
+52,UTIL_R7,2.574088
+53,CONS_R7,0.615061
+54,MANU_R7,3.1915
+55,COMMER_R7,492.5688
+56,EDU_R7,1.132406
+57,HEALTH_R7,111.1063
+58,ART_ACC_R7,19.894
+59,RELIG_R7,10.45709
+60,HS1_R1,1951.061
+61,HS2_R1,293.8371
+62,HS1_R2,2798.116
+63,HS2_R2,1491.191
+64,HS1_R3,4080.699
+65,HS2_R3,1132.364
+66,HS1_R4,3968.163
+67,HS2_R4,1036.572
+68,HS1_R5,6457.689
+69,HS2_R5,1235.736
+70,HS1_R6,3945.569
+71,HS2_R6,702.0965
+72,HS1_R7,2574.569
+73,HS2_R7,310.0058
diff --git a/pyincore/analyses/mlenabledcgeslc/mlcgeslc.py b/pyincore/analyses/mlenabledcgeslc/mlcgeslc.py
new file mode 100644
index 000000000..6d1ca4e1b
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/mlcgeslc.py
@@ -0,0 +1,179 @@
+# Copyright (c) 2024 University of Illinois and others. All rights reserved.
+#
+# This program and the accompanying materials are made available under the
+# terms of the Mozilla Public License v2.0 which accompanies this distribution,
+# and is available at https://www.mozilla.org/en-US/MPL/2.0/
+
+import numpy as np
+import os
+import pandas as pd
+
+from pyincore import globals as pyglobals
+from pyincore.client import IncoreClient
+from pyincore.analyses.core_cge_ml import CoreCGEML
+from pyincore.utils import parse_files
+
+logger = pyglobals.LOGGER
+
+
+class MlEnabledCgeSlc(CoreCGEML):
+
+    model = "Machine Learning Enabled Computable General Equilibrium - Salt Lake City "
+
+    # Coefficients files
+    DDS_coefficients_file = "DDS_coefficients.csv"
+    DY_coefficients_file = "DY_coefficients.csv"
+    MIGT_coefficients_file = "MIGT_coefficients.csv"
+    DFFD_coefficients_file = "DFFD_coefficients.csv"
+
+    # Base value files
+    DS_base_val_file = "DS_base_val.csv"
+    GI_base_val_file = "GI_base_val.csv"
+    HH_base_val_file = "HH_base_val.csv"
+    FD_base_val_file = "FD_base_val.csv"
+
+    Base_KAP_file = "baseKAP.csv"
+
+    base_file_path = os.path.join(
+        pyglobals.PYINCORE_PACKAGE_HOME,
+        "analyses",
+        "mlenabledcgeslc",
+    )
+
+    model_filenames = {
+        "ds": os.path.join(
+            base_file_path,
+            DDS_coefficients_file,
+        ),
+        "dy": os.path.join(
+            base_file_path,
+            DY_coefficients_file,
+        ),
+        "migt": os.path.join(
+            base_file_path,
+            MIGT_coefficients_file,
+        ),
+        "dffd": os.path.join(
+            base_file_path,
+            DFFD_coefficients_file,
+        ),
+    }
+
+    filenames = [
+        os.path.join(
+            base_file_path,
+            DS_base_val_file,
+        ),
+        os.path.join(
+            base_file_path,
+            GI_base_val_file,
+        ),
+        os.path.join(
+            base_file_path,
+            HH_base_val_file,
+        ),
+        os.path.join(
+            base_file_path,
+            FD_base_val_file,
+        ),
+        os.path.join(
+            base_file_path,
+            Base_KAP_file,
+        ),
+    ]
+
+    def __init__(self, incore_client: IncoreClient):
+        sectors, base_cap_factors, base_cap, model_coeffs, cap_shock_sectors = (
+            parse_files(self.model_filenames, self.filenames)
+        )
+        self.base_cap_factors = base_cap_factors
+        self.base_cap = base_cap
+        self.model_coeffs = model_coeffs
+        self.cap_shock_sectors = cap_shock_sectors
+        super(MlEnabledCgeSlc, self).__init__(
+            incore_client, sectors, labor_groups=[f"L{gp}" for gp in range(1, 5)]
+        )  # 4 labor groups
+
+    def run(self) -> bool:
+        logger.info(f"Running {self.model} model...")
+        sector_shocks = pd.read_csv(
+            self.get_input_dataset("sector_shocks").get_file_path("csv")
+        )
+        # arrange the capital shocks in the same order as the sectors
+        shocks = []
+
+        for sector in self.cap_shock_sectors:
+            if sector.upper() not in [v.upper() for v in sector_shocks["sector"]]:
+                raise ValueError(
+                    f"Sector {sector} not found in the sector shocks file with\n {sector_shocks['sector']} sectors.\n" + 
+                    "Please make sure you have used the correct capital shocks"
+                )
+            shocks.append(
+                sector_shocks.loc[sector_shocks["sector"] == sector.upper()]["shock"]
+            )
+        capital_shocks = np.array(shocks, dtype=np.float64).reshape(1, -1)
+        # logger.info(f"capital_shocks shape: {capital_shocks.shape}")
+
+        super().run_core_cge_ml(
+            self.base_cap,
+            capital_shocks,
+            self.model_coeffs,
+            self.base_cap_factors,
+        )
+        logger.info(f"Running {self.model} model completed.")
+
+        return True
+
+    def get_spec(self):
+        return {
+            "name": "Salt-Lake-cge",
+            "description": "CGE model for Salt Lake City.",
+            "input_parameters": [
+                {
+                    "id": "result_name",
+                    "required": False,
+                    "description": "Result CSV dataset name prefix",
+                    "type": str,
+                }
+            ],
+            "input_datasets": [
+                {
+                    "id": "sector_shocks",
+                    "required": True,
+                    "description": "Aggregation of building functionality states to capital shocks per sector",
+                    "type": ["incore:capitalShocks"],
+                }
+            ],
+            "output_datasets": [
+                {
+                    "id": "domestic-supply",
+                    "parent_type": "",
+                    "description": "CSV file of resulting domestic supply",
+                    "type": "incore:Employment",
+                },
+                {
+                    "id": "gross-income",
+                    "parent_type": "",
+                    "description": "CSV file of resulting gross income",
+                    "type": "incore:Employment",
+                },
+                {
+                    "id": "pre-disaster-factor-demand",
+                    "parent_type": "",
+                    "description": "CSV file of factor demand before disaster",
+                    "type": "incore:FactorDemand",
+                },
+                {
+                    "id": "post-disaster-factor-demand",
+                    "parent_type": "",
+                    "description": "CSV file of resulting factor-demand",
+                    "type": "incore:FactorDemand",
+                },
+                {
+                    "id": "household-count",
+                    "parent_type": "",
+                    "description": "CSV file of household count",
+                    "type": "incore:HouseholdCount",
+                },
+            ],
+        }
diff --git a/pyincore/utils/__init__.py b/pyincore/utils/__init__.py
index 93eb09efa..c8d0703bd 100644
--- a/pyincore/utils/__init__.py
+++ b/pyincore/utils/__init__.py
@@ -1 +1,2 @@
-from pyincore.utils.http_util import return_http_response
\ No newline at end of file
+from pyincore.utils.http_util import return_http_response
+from pyincore.utils.cge_ml_file_util import parse_coeff, parse_csv, parse_base_vals, parse_files
\ No newline at end of file
diff --git a/pyincore/utils/cge_ml_file_util.py b/pyincore/utils/cge_ml_file_util.py
new file mode 100644
index 000000000..f16319d3e
--- /dev/null
+++ b/pyincore/utils/cge_ml_file_util.py
@@ -0,0 +1,159 @@
+from typing import Tuple, List, Dict
+
+import pandas as pd
+import numpy as np
+import os
+
+from pyincore import globals as pyglobals
+
+logger = pyglobals.LOGGER
+
+
+def parse_coeff(
+    filenames: Dict[str, str]
+) -> Tuple[Dict[str, np.ndarray], Dict[str, List[str]], Dict[str, List[str]]]:
+    """parse_coeff Function to parse the model coefficients.
+
+    Parameters
+    ----------
+    filenames : Dict[str, str]
+        Dictionary containing the factor name and the path to the coefficient file
+
+    Returns
+    -------
+    Tuple[Dict[str, np.ndarray], Dict[str, List[str]], Dict[str, List[str]]]
+        Tuple containing three dictionaries. The first dictionary contains the model coefficients for
+        each factor, the second dictionary contains the sectors for each factor, and the third dictionary
+        contains the order of the sectors for each factor for models.
+    """
+
+    model_coeffs: Dict[str, np.ndarray] = {}
+    sectors: Dict[str, List[str]] = {}
+    base_cap_sector_order: Dict[str, List[str]] = {}
+
+    for factor, filename in filenames.items():
+        logger.info(f"Parsing {filename}")
+        model_coeff_df: pd.DataFrame = pd.read_csv(filename)
+        sectors[factor] = list(model_coeff_df.columns[5:])
+        base_cap_sector_order[factor] = [
+            s.split(" ")[-1].upper() for s in list(model_coeff_df["Model_Name"])[1:]
+        ]
+        model_coeffs[factor] = np.float64(
+            model_coeff_df[model_coeff_df.columns[4:]].to_numpy()[1:, :]
+        )  # skip the first row as it contains the total value model and its not needed in output
+        # logger.info(f"factor: {factor} - model_coeff shape: {model_coeffs[factor].shape}")
+
+    return model_coeffs, sectors, base_cap_sector_order
+
+
+def parse_csv(file_name: str, sectors: List[str]) -> np.ndarray:
+    """parse_csv Utility function to parse the csv files
+
+    Parameters
+    ----------
+    file_name : str
+        Path to the csv file
+
+    Returns
+    -------
+    np.ndarray
+        Numpy array containing the data from the csv file
+    """
+
+    logger.info(f"Parsing {file_name}")
+    df = pd.read_csv(file_name)
+
+    col_name = df.columns[1]
+    val_col = df.columns[2]
+
+    # Convert 'sectors' column to categorical with the desired order
+    df[col_name] = pd.Categorical(df[col_name], categories=sectors, ordered=True)
+
+    # Sort the DataFrame based on the 'names' column
+    df = df.sort_values(by=col_name)
+
+    return np.array(df[val_col], dtype=np.float64)
+
+
+def parse_base_vals(
+    filenames: List[str],
+    ds_sectors: List[str],
+    base_cap_sector_order: Dict[str, List[str]],
+) -> Tuple[List[np.ndarray], np.ndarray]:
+    """parse_base_vals parse_base_vals will parse the base values from the input file and return them as numpy arrays
+
+    Parameters
+    ----------
+    filenames : List[str]
+        Paths to the .csv files containing the base values. It has to be organized this order, starting from:
+        1. Domestic Supply
+        2. Gross Income
+        3. Household Count
+        4. Factor Demand
+        5. Base Capital
+
+    Returns
+    -------
+
+    Tuple[np.ndarray, np.ndarray]
+        Tuple containing two numpy arrays. The first array is the base capital for different factors and the second is the base capital
+    """
+
+    base_cap_factors: List[np.ndarray] = []
+    base_cap: np.ndarray = parse_csv(filenames[-1], ds_sectors).reshape(
+        1, -1
+    )  # 1 x K array K = number of sectors in the model
+    # logger.info(f"base_cap shape: {base_cap.shape}")
+
+    for filename, sector_order in zip(filenames[:-1], base_cap_sector_order.values()):
+        base_cap_factors.append(
+            parse_csv(filename, sector_order).reshape(-1, 1)
+        )  # k_i x 1 array k_i = number of sectors k for a factor i
+        # logger.info(f"{os.path.basename(filename)} shape: {base_cap_factors[-1].shape}")
+
+    return base_cap_factors, base_cap
+
+
+def parse_files(
+    model_filenames: Dict[str, str], filenames: List[str]
+) -> Tuple[Dict[str, List[str]], np.ndarray, np.ndarray, Dict[str, np.ndarray]]:
+    """parse_files Utility function to parse the input files
+
+    Parameters
+    ----------
+    model_filenames : Dict[str, str]
+        Dictionary containing the factor name and the path to the coefficient file
+    filenames : List[str]
+        Paths to the .csv files containing the base values. It has to be organized this order, starting from:
+            1. Domestic Supply
+            2. Gross Income
+            3. Household Count
+            4. Factor Demand
+            5. Base Capital
+
+    Returns
+    -------
+    Tuple[Dict[str, List[str]], np.ndarray, np.ndarray, Dict[str, np.ndarray]]
+        Returns a tuple containing the following:
+        1. sectors: Dictionary containing the sectors for each factor
+        2. base_cap_factors: List of numpy arrays containing the base capital for each factor
+        3. base_cap: Numpy array containing the base capital
+        4. model_coeffs: Dictionary containing the model coefficients for each factor
+        5. sectors["ds"]: List of sectors for the domestic supply
+    """
+    logger.info("Parsing input files...")
+
+    model_coeffs, sectors, base_cap_sector_ordering = parse_coeff(model_filenames)
+
+    base_cap_factors, base_cap = parse_base_vals(
+        filenames, sectors["ds"], base_cap_sector_ordering
+    )
+    logger.info("Parsing input files completed.")
+
+    return (
+        base_cap_sector_ordering,
+        base_cap_factors,
+        base_cap,
+        model_coeffs,
+        sectors["ds"],
+    )
diff --git a/requirements.txt b/requirements.txt
index 1c769cd14..67803f1a7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,4 +14,4 @@ rasterio>=1.3.9
 requests>=2.31.0
 rtree>=1.1.0
 scipy>=1.11.3
-shapely>=2.0.2
\ No newline at end of file
+shapely>=2.0.2
diff --git a/setup.py b/setup.py
index 3117f5375..675317bed 100644
--- a/setup.py
+++ b/setup.py
@@ -49,7 +49,7 @@
     packages=find_packages(where=".", exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
     include_package_data=True,
     package_data={
-        '': ['*.ini']
+        '': ['*.ini', "*.csv"]
     },
 
     python_requires=">=3.9",
diff --git a/tests/pyincore/analyses/mlenabledcgeslc/test_mlcgeslc.py b/tests/pyincore/analyses/mlenabledcgeslc/test_mlcgeslc.py
new file mode 100644
index 000000000..9f90de2c9
--- /dev/null
+++ b/tests/pyincore/analyses/mlenabledcgeslc/test_mlcgeslc.py
@@ -0,0 +1,14 @@
+from pyincore import IncoreClient, globals as pyglobals
+from pyincore.analyses.mlenabledcgeslc import MlEnabledCgeSlc
+
+client = IncoreClient(pyglobals.INCORE_API_DEV_URL)
+
+mlcgeslc = MlEnabledCgeSlc(client)
+
+sector_shocks = "65fdeed7e42f3b0da56c4eef"
+
+mlcgeslc.load_remote_input_dataset("sector_shocks", sector_shocks)
+# optional
+mlcgeslc.set_parameter("result_name", "slc_7_region")
+
+mlcgeslc.run_analysis()
\ No newline at end of file
diff --git a/tests/pyincore/utils/test_dataprocessutil.py b/tests/pyincore/utils/test_dataprocessutil.py
index 3d36c0cb5..83b888cf9 100644
--- a/tests/pyincore/utils/test_dataprocessutil.py
+++ b/tests/pyincore/utils/test_dataprocessutil.py
@@ -15,7 +15,7 @@ def client():
 def test_get_mapped_result(client):
     bldg_dataset_id = "5f9091df3e86721ed82f701d"
     bldg_dmg_dataset_id = "5f9868c00ace240b22a7f2a5"
-    bldg_func_state_dataset_id = "642f66e5f27db6680103c4ad"
+    bldg_func_state_dataset_id = "660d9435ce705a7e547a664e"
     archetype_id = "5fca915fb34b193f7a44059b"
 
     dmg_ret_json, func_ret_json, max_state_df = util.get_mapped_result_from_dataset_id(
@@ -35,7 +35,7 @@ def test_get_mapped_result_from_analysis(client):
 
     archetype_id = "5fca915fb34b193f7a44059b"
 
-    bldg_func_state_dataset_id = "642f66e5f27db6680103c4ad"
+    bldg_func_state_dataset_id = "660d9435ce705a7e547a664e"
     bldg_func_state_dataset = Dataset.from_data_service(bldg_func_state_dataset_id, DataService(client))
 
     dmg_ret_json, func_ret_json, max_state_df = util.get_mapped_result_from_analysis(
@@ -90,7 +90,7 @@ def test_joplin_mcs_cluster(client):
 def test_joplin_bldg_func_cluster(client):
     _functionality_cluster(client, archetype_mapping="5fca915fb34b193f7a44059b",
                            building_dataset_id="5fa0b132cc6848728b66948d",
-                           bldg_func_state_id="642f66e5f27db6680103c4ad",
+                           bldg_func_state_id="660d9435ce705a7e547a664e",
                            arch_column="archetype",
                            title="joplin_bldg_func")
 
@@ -98,7 +98,7 @@ def test_joplin_bldg_func_cluster(client):
 def test_galveston_mcs_cluster(client):
     _functionality_cluster(client, archetype_mapping="6420befbb18d026e7c7dbafc",
                            building_dataset_id="63ff69a96d3b2a308baaca12",
-                           bldg_func_state_id="642f6230f27db66801038964",
+                           bldg_func_state_id="660d95bece705a7e547a6654",
                            arch_column="arch_wind",
                            title="galveston_mcs")
 
@@ -106,6 +106,6 @@ def test_galveston_mcs_cluster(client):
 def test_galveston_bldg_func_cluster(client):
     _functionality_cluster(client, archetype_mapping="6420befbb18d026e7c7dbafc",
                            building_dataset_id="63ff69a96d3b2a308baaca12",
-                           bldg_func_state_id="642f6495f27db66801039434",
+                           bldg_func_state_id="660d97bfce705a7e547a6659",
                            arch_column="arch_wind",
                            title="galveston_bldg_func")