diff --git a/Pipfile b/Pipfile
index eb79391..0c44e8f 100644
--- a/Pipfile
+++ b/Pipfile
@@ -10,6 +10,8 @@ pytest = "*"
 dtuprosys = "*"
 cairocffi = "*"
 numba = "*"
+polars = "*"
+pyarrow = "*"
 
 [dev-packages]
 black = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
index eb86d46..08066d8 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "5e52a7db84f1aefd13d2b53cf2ec5ce57a952b4254b6e66d890d21c5e53262d5"
+            "sha256": "98d4fd303175dc12324e72398ec3ad23632701e8a37c3a41a41c94d3e54ec854"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -16,20 +16,13 @@
         ]
     },
     "default": {
-        "attrs": {
-            "hashes": [
-                "sha256:29e95c7f6778868dbd49170f98f8818f78f3dc5e0e37c0b1f474e3561b240836",
-                "sha256:c9227bfc2f01993c03f68db37d1d15c9690188323c067c641f1a35ca58185f99"
-            ],
-            "markers": "python_version >= '3.6'",
-            "version": "==22.2.0"
-        },
         "cairocffi": {
             "hashes": [
                 "sha256:78e6bbe47357640c453d0be929fa49cd05cce2e1286f3d2a1ca9cbda7efdb8b7",
                 "sha256:aa78ee52b9069d7475eeac457389b6275aa92111895d78fbaa2202a52dac112e"
             ],
             "index": "pypi",
+            "markers": "python_version >= '3.7'",
             "version": "==1.6.1"
         },
         "cffi": {
@@ -154,6 +147,7 @@
                 "sha256:d92a966135d13ad9a30d3115ea38701785abde92642a467b45a1429d34d395bf"
             ],
             "index": "pypi",
+            "markers": "python_version >= '3.7'",
             "version": "==0.1.24"
         },
         "exceptiongroup": {
@@ -166,51 +160,51 @@
         },
         "fonttools": {
             "hashes": [
-                "sha256:13ac0cba2fc63fa4b232f2a7971f35f35c6eaf10bd1271fa96d4ce6253a8acfd",
-                "sha256:156ae342a1ed1fe38e180de471e98fbf5b2b6ae280fa3323138569c4ca215844",
-                "sha256:1a9f9cdd7ef63d1b8ac90db335762451452426b3207abd79f60da510cea62da5",
-                "sha256:1c9937c4dd1061afd22643389445fabda858af5e805860ec3082a4bc07c7a720",
-                "sha256:25852f0c63df0af022f698464a4a80f7d1d5bd974bcd22f995f6b4ad198e32dd",
-                "sha256:2ae45716c27a41807d58a9f3f59983bdc8c0a46cb259e4450ab7e196253a9853",
-                "sha256:2c23c59d321d62588620f2255cf951270bf637d88070f38ed8b5e5558775b86c",
-                "sha256:2cf923a4a556ab4cc4c52f69a4a2db624cf5a2cf360394368b40c5152fe3321e",
-                "sha256:2d0eba685938c603f2f648dfc0aadbf8c6a4fe1c7ca608c2970a6ef39e00f254",
-                "sha256:3033b55f401a622de2630b3982234d97219d89b058607b87927eccb0f922313c",
-                "sha256:49ea0983e55fd7586a809787cd4644a7ae471e53ab8ddc016f9093b400e32646",
-                "sha256:5200b01f463d97cc2b7ff8a1e3584151f4413e98cb8419da5f17d1dbb84cc214",
-                "sha256:5b627ed142398ea9202bd752c04311592558964d1a765fb2f78dc441a05633f4",
-                "sha256:6d4a4ebcc76e30898ff3296ea786491c70e183f738319ae2629e0d44f17ece42",
-                "sha256:795150d5edc595e1a2cfb3d65e8f4f3d027704fc2579f8990d381bef6b188eb6",
-                "sha256:7b460720ce81773da1a3e7cc964c48e1e11942b280619582a897fa0117b56a62",
-                "sha256:7b5636f5706d49f13b6d610fe54ee662336cdf56b5a6f6683c0b803e23d826d2",
-                "sha256:8485cc468288e213f31afdaf1fdda3c79010f542559fbba936a54f4644df2570",
-                "sha256:87c214197712cc14fd2a4621efce2a9c501a77041232b789568149a8a3161517",
-                "sha256:87c3299da7da55394fb324349db0ede38114a46aafd0e7dfcabfecd28cdd94c3",
-                "sha256:89c2c520f9492844ecd6316d20c6c7a157b5c0cb73a1411b3db28ee304f30122",
-                "sha256:8be6adfa4e15977075278dd0a0bae74dec59be7b969b5ceed93fb86af52aa5be",
-                "sha256:8bee9f4fc8c99824a424ae45c789ee8c67cb84f8e747afa7f83b7d3cef439c3b",
-                "sha256:982f69855ac258260f51048d9e0c53c5f19881138cc7ca06deb38dc4b97404b6",
-                "sha256:9e6aeb5c340416d11a3209d75c48d13e72deea9e1517837dd1522c1fd1f17c11",
-                "sha256:a0e94244ec24a940ecfbe5b31c975c8a575d5ed2d80f9a280ce3b21fa5dc9c34",
-                "sha256:a4a50a1dfad7f7ba5ca3f99cc73bf5cdac67ceade8e4b355a877521f20ad1b63",
-                "sha256:a9fa52ef8fd14d7eb3d813e1451e7ace3e1eebfa9b7237d3f81fee8f3de6a114",
-                "sha256:adab73618d0a328b203a0e242b3eba60a2b5662d9cb2bd16ed9c52af8a7d86af",
-                "sha256:c506e3d3a9e898caee4dc094f34b49c5566870d5a2d1ca2125f0a9f35ecc2205",
-                "sha256:c779f8701deedf41908f287aeb775b8a6f59875ad1002b98ac6034ae4ddc1b7b",
-                "sha256:c94564b1f3b5dd87e73577610d85115b1936edcc596deaf84a31bbe70e17456b",
-                "sha256:c9a0e422ab79e5cb2b47913be6a4b5fd20c4c7ac34a24f3691a4e099e965e0b8",
-                "sha256:ca9eceebe70035b057ce549e2054cad73e95cac3fe91a9d827253d1c14618204",
-                "sha256:ce199227ce7921eaafdd4f96536f16b232d6b580ce74ce337de544bf06cb2752",
-                "sha256:d00fc63131dcac6b25f50a5a129758438317e54e3ce5587163f7058de4b0e933",
-                "sha256:d3d7b96aba96e05e8c911ce2dfc5acc6a178b8f44f6aa69371ab91aa587563da",
-                "sha256:d4e69e2c7f93b695d2e6f18f709d501d945f65c1d237dafaabdd23cd935a5276",
-                "sha256:e26e7fb908ae4f622813e7cb32cd2db6c24e3122bb3b98f25e832a2fe0e7e228",
-                "sha256:e5b7905fd68eacb7cc56a13139da5c312c45baae6950dd00b02563c54508a041",
-                "sha256:f5f1423a504ccc329efb5aa79738de83d38c072be5308788dde6bd419969d7f5",
-                "sha256:f8bc3973ed58893c4107993e0a7ae34901cb572b5e798249cbef35d30801ffd4"
+                "sha256:0452fcbfbce752ba596737a7c5ec5cf76bc5f83847ce1781f4f90eab14ece252",
+                "sha256:0a2417547462e468edf35b32e3dd06a6215ac26aa6316b41e03b8eeaf9f079ea",
+                "sha256:0d2b01428f7da26f229a5656defc824427b741e454b4e210ad2b25ed6ea2aed4",
+                "sha256:0d533f89819f9b3ee2dbedf0fed3825c425850e32bdda24c558563c71be0064e",
+                "sha256:12ee86abca46193359ea69216b3a724e90c66ab05ab220d39e3fc068c1eb72ac",
+                "sha256:18b35fd1a850ed7233a99bbd6774485271756f717dac8b594958224b54118b61",
+                "sha256:292922dc356d7f11f5063b4111a8b719efb8faea92a2a88ed296408d449d8c2e",
+                "sha256:2eb4167bde04e172a93cf22c875d8b0cff76a2491f67f5eb069566215302d45d",
+                "sha256:3cdb9a92521b81bf717ebccf592bd0292e853244d84115bfb4db0c426de58348",
+                "sha256:4108b1d247953dd7c90ec8f457a2dec5fceb373485973cc852b14200118a51ee",
+                "sha256:4709c5bf123ba10eac210d2d5c9027d3f472591d9f1a04262122710fa3d23199",
+                "sha256:5057ade278e67923000041e2b195c9ea53e87f227690d499b6a4edd3702f7f01",
+                "sha256:56339ec557f0c342bddd7c175f5e41c45fc21282bee58a86bd9aa322bec715f2",
+                "sha256:578c00f93868f64a4102ecc5aa600a03b49162c654676c3fadc33de2ddb88a81",
+                "sha256:594206b31c95fcfa65f484385171fabb4ec69f7d2d7f56d27f17db26b7a31814",
+                "sha256:63c73b9dd56a94a3cbd2f90544b5fca83666948a9e03370888994143b8d7c070",
+                "sha256:63dc592a16cd08388d8c4c7502b59ac74190b23e16dfc863c69fe1ea74605b68",
+                "sha256:6978bade7b6c0335095bdd0bd97f8f3d590d2877b370f17e03e0865241694eb5",
+                "sha256:6f30e605c7565d0da6f0aec75a30ec372072d016957cd8fc4469721a36ea59b7",
+                "sha256:702ae93058c81f46461dc4b2c79f11d3c3d8fd7296eaf8f75b4ba5bbf813cd5f",
+                "sha256:8b8a45254218679c7f1127812761e7854ed5c8e34349aebf581e8c9204e7495a",
+                "sha256:902e9c4e9928301912f34a6638741b8ae0b64824112b42aaf240e06b735774b1",
+                "sha256:97f0a49fa6aa2d6205c6f72f4f98b74ef4b9bfdcb06fd78e6fe6c7af4989b63e",
+                "sha256:9b4ec6d42a7555f5ae35f3b805482f0aad0f1baeeef54859492ea3b782959d4a",
+                "sha256:9b58638d8a85e3a1b32ec0a91d9f8171a877b4b81c408d4cb3257d0dee63e092",
+                "sha256:a8c8b54bd1420c184a995f980f1a8076f87363e2bb24239ef8c171a369d85a31",
+                "sha256:aee76fd81a8571c68841d6ef0da750d5ff08ff2c5f025576473016f16ac3bcf7",
+                "sha256:b10633aafc5932995a391ec07eba5e79f52af0003a1735b2306b3dab8a056d48",
+                "sha256:bcd77f89fc1a6b18428e7a55dde8ef56dae95640293bfb8f4e929929eba5e2a2",
+                "sha256:bff5b38d0e76eb18e0b8abbf35d384e60b3371be92f7be36128ee3e67483b3ec",
+                "sha256:c900508c46274d32d308ae8e82335117f11aaee1f7d369ac16502c9a78930b0a",
+                "sha256:cad5cfd044ea2e306fda44482b3dd32ee47830fa82dfa4679374b41baa294f5f",
+                "sha256:cdfd7557d1bd294a200bd211aa665ca3b02998dcc18f8211a5532da5b8fad5c5",
+                "sha256:cf5a0cd974f85a80b74785db2d5c3c1fd6cc09a2ba3c837359b2b5da629ee1b0",
+                "sha256:d10979ef14a8beaaa32f613bb698743f7241d92f437a3b5e32356dfb9769c65d",
+                "sha256:d20588466367f05025bb1efdf4e5d498ca6d14bde07b6928b79199c588800f0a",
+                "sha256:d3260db55f1843e57115256e91247ad9f68cb02a434b51262fe0019e95a98738",
+                "sha256:df48798f9a4fc4c315ab46e17873436c8746f5df6eddd02fad91299b2af7af95",
+                "sha256:e3e33862fc5261d46d9aae3544acb36203b1a337d00bdb5d3753aae50dac860e",
+                "sha256:e740a7602c2bb71e1091269b5dbe89549749a8817dc294b34628ffd8b2bf7124",
+                "sha256:f40441437b039930428e04fb05ac3a132e77458fb57666c808d74a556779e784",
+                "sha256:f7449493886da6a17472004d3818cc050ba3f4a0aa03fb47972e4fa5578e6703"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==4.46.0"
+            "version": "==4.48.1"
         },
         "iniconfig": {
             "hashes": [
@@ -340,33 +334,30 @@
         },
         "llvmlite": {
             "hashes": [
-                "sha256:04725975e5b2af416d685ea0769f4ecc33f97be541e301054c9f741003085802",
-                "sha256:0dd0338da625346538f1173a17cabf21d1e315cf387ca21b294ff209d176e244",
-                "sha256:150d0bc275a8ac664a705135e639178883293cf08c1a38de3bbaa2f693a0a867",
-                "sha256:1eee5cf17ec2b4198b509272cf300ee6577229d237c98cc6e63861b08463ddc6",
-                "sha256:210e458723436b2469d61b54b453474e09e12a94453c97ea3fbb0742ba5a83d8",
-                "sha256:2181bb63ef3c607e6403813421b46982c3ac6bfc1f11fa16a13eaafb46f578e6",
-                "sha256:24091a6b31242bcdd56ae2dbea40007f462260bc9bdf947953acc39dffd54f8f",
-                "sha256:2b76acee82ea0e9304be6be9d4b3840208d050ea0dcad75b1635fa06e949a0ae",
-                "sha256:2d92c51e6e9394d503033ffe3292f5bef1566ab73029ec853861f60ad5c925d0",
-                "sha256:5940bc901fb0325970415dbede82c0b7f3e35c2d5fd1d5e0047134c2c46b3281",
-                "sha256:8454c1133ef701e8c050a59edd85d238ee18bb9a0eb95faf2fca8b909ee3c89a",
-                "sha256:855f280e781d49e0640aef4c4af586831ade8f1a6c4df483fb901cbe1a48d127",
-                "sha256:880cb57ca49e862e1cd077104375b9d1dfdc0622596dfa22105f470d7bacb309",
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+                "sha256:bb3975787f13eb97629052edb5017f6c170eebc1c14a0433e8089e5db43bcce6",
+                "sha256:bdd3888544538a94d7ec99e7c62a0cdd8833609c85f0c23fcb6c5c591aec60ad",
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+                "sha256:c5bece0cdf77f22379f19b1959ccd7aee518afa4afbd3656c6365865f84903f9",
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+                "sha256:ebe66a86dc44634b59a3bc860c7b20d26d9aaffcd30364ebe8ba79161a9121f4",
+                "sha256:f92b09243c0cc3f457da8b983f67bd8e1295d0f5b3746c7a1861d7a99403854a"
             ],
-            "markers": "python_version >= '3.8'",
-            "version": "==0.41.1"
+            "markers": "python_version >= '3.9'",
+            "version": "==0.42.0"
         },
         "matplotlib": {
             "hashes": [
@@ -412,180 +403,255 @@
         },
         "numba": {
             "hashes": [
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@@ -604,11 +670,12 @@
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@@ -620,73 +687,69 @@
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@@ -698,11 +761,11 @@
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@@ -714,21 +777,21 @@
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@@ -791,58 +854,56 @@
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@@ -854,11 +915,11 @@
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@@ -1024,35 +1085,39 @@
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@@ -1088,10 +1153,10 @@
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@@ -1100,6 +1165,30 @@
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@@ -1110,34 +1199,27 @@
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@@ -1156,18 +1238,19 @@
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@@ -1177,20 +1260,23 @@
             "version": "==2.4"
         },
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@@ -1203,11 +1289,11 @@
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@@ -1219,51 +1305,51 @@
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@@ -1470,72 +1548,65 @@
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@@ -1567,46 +1638,13 @@
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-                "sha256:1ea08a89b835dd1b8c0c900d92848147cef2537243361b2e3f4dc15df9b6fded",
-                "sha256:87b0d96642d0fe5f5abd7783857b9cab167f221a39ff98e3b9619a788a3c0f2e"
+                "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0",
+                "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==0.18.0"
+            "version": "==0.18.1"
         },
         "tinycss2": {
             "hashes": [
@@ -2006,26 +2045,27 @@
         },
         "traitlets": {
             "hashes": [
-                "sha256:f14949d23829023013c47df20b4a76ccd1a85effb786dc060f34de7948361b33",
-                "sha256:fcdaa8ac49c04dfa0ed3ee3384ef6dfdb5d6f3741502be247279407679296772"
+                "sha256:8cdd83c040dab7d1dee822678e5f5d100b514f7b72b01615b26fc5718916fdf9",
+                "sha256:fcdf85684a772ddeba87db2f398ce00b40ff550d1528c03c14dbf6a02003cd80"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==5.14.0"
+            "version": "==5.14.2"
         },
         "types-python-dateutil": {
             "hashes": [
-                "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b",
-                "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"
+                "sha256:78aa9124f360df90bb6e85eb1a4d06e75425445bf5ecb13774cb0adef7ff3956",
+                "sha256:c1f6310088eb9585da1b9f811765b989ed2e2cdd4203c1a367e944b666507e4e"
             ],
-            "version": "==2.8.19.14"
+            "markers": "python_version >= '3.8'",
+            "version": "==2.9.0.20240315"
         },
         "typing-extensions": {
             "hashes": [
-                "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0",
-                "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"
+                "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475",
+                "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"
             ],
             "markers": "python_version < '3.11'",
-            "version": "==4.8.0"
+            "version": "==4.10.0"
         },
         "uri-template": {
             "hashes": [
@@ -2036,18 +2076,18 @@
         },
         "urllib3": {
             "hashes": [
-                "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3",
-                "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"
+                "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d",
+                "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==2.1.0"
+            "version": "==2.2.1"
         },
         "wcwidth": {
             "hashes": [
-                "sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02",
-                "sha256:f26ec43d96c8cbfed76a5075dac87680124fa84e0855195a6184da9c187f133c"
+                "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859",
+                "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"
             ],
-            "version": "==0.2.12"
+            "version": "==0.2.13"
         },
         "webcolors": {
             "hashes": [
@@ -2073,11 +2113,11 @@
         },
         "widgetsnbextension": {
             "hashes": [
-                "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385",
-                "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"
+                "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f",
+                "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==4.0.9"
+            "version": "==4.0.10"
         }
     }
 }
diff --git a/chemotools/datasets/_base.py b/chemotools/datasets/_base.py
index 91d0167..6906f3a 100644
--- a/chemotools/datasets/_base.py
+++ b/chemotools/datasets/_base.py
@@ -1,14 +1,22 @@
-import pandas as pd
 import os
 
+
+import pandas as pd
+import polars as pl
+
 PACKAGE_DIRECTORY = os.path.dirname(os.path.abspath(__file__))
 
 
-def load_fermentation_train():
+def load_fermentation_train(set_output="pandas"):
     """
-    Loads the training data of the fermentation dataset. This data corresponds to a synthetic dataset measured 
+    Loads the training data of the fermentation dataset. This data corresponds to a synthetic dataset measured
     off-line. This dataset is designed to represent the variability of real fermentation data.
 
+    Arguments
+    -------
+    set_output: str, default='pandas'
+        The output format of the data. It can be 'pandas' or 'polars'. If 'polars', the data is returned as a polars DataFrame.
+
     Returns
     -------
     train_spectra: pd.DataFrame A pandas DataFrame containing the synthetic spectra measured to train the model.
@@ -20,17 +28,32 @@ def load_fermentation_train():
     Mauricio Iglesias Miguel, Gernaey Krist V. Transforming data into information:
     A parallel hybrid model for real-time state estimation in lignocellulose ethanol fermentations.
     """
-    train_spectra = pd.read_csv(PACKAGE_DIRECTORY + "/data/train_spectra.csv")
-    train_spectra.columns = train_spectra.columns.astype(float)
-    train_hplc = pd.read_csv(PACKAGE_DIRECTORY + "/data/train_hplc.csv")
+    if set_output == "pandas":
+        train_spectra = pd.read_csv(PACKAGE_DIRECTORY + "/data/train_spectra.csv")
+        train_spectra.columns = train_spectra.columns.astype(float)
+        train_hplc = pd.read_csv(PACKAGE_DIRECTORY + "/data/train_hplc.csv")
+        return train_spectra, train_hplc
 
-    return train_spectra, train_hplc
+    if set_output == "polars":
+        train_spectra = pl.read_csv(PACKAGE_DIRECTORY + "/data/train_spectra.csv")
+        train_hplc = pl.read_csv(PACKAGE_DIRECTORY + "/data/train_hplc.csv")
+        return train_spectra, train_hplc
 
+    else:
+        raise ValueError(
+            "Invalid value for set_output. Please use 'pandas' or 'polars'."
+        )
 
-def load_fermentation_test():
+
+def load_fermentation_test(set_output="pandas"):
     """
     Loads the testing data of the fermentation dataset. This data corresponds to real fermentation data measured
-    on-line during a fermentation process. 
+    on-line during a fermentation process.
+
+    Arguments
+    -------
+    set_output: str, default='pandas'
+        The output format of the data. It can be 'pandas' or 'polars'. If 'polars', the data is returned as a polars DataFrame.
 
     Returns
     -------
@@ -43,27 +66,57 @@ def load_fermentation_test():
     Mauricio Iglesias Miguel, Gernaey Krist V. Transforming data into information:
     A parallel hybrid model for real-time state estimation in lignocellulose ethanol fermentations.
     """
-    fermentation_spectra = pd.read_csv(
-        PACKAGE_DIRECTORY + "/data/fermentation_spectra.csv"
-    )
-    fermentation_spectra.columns = fermentation_spectra.columns.astype(float)
-    fermentation_hplc = pd.read_csv(PACKAGE_DIRECTORY + "/data/fermentation_hplc.csv")
-
-    return fermentation_spectra, fermentation_hplc
-
-
-def load_coffee():
+    if set_output == "pandas":
+        fermentation_spectra = pd.read_csv(
+            PACKAGE_DIRECTORY + "/data/fermentation_spectra.csv"
+        )
+        fermentation_spectra.columns = fermentation_spectra.columns.astype(float)
+        fermentation_hplc = pd.read_csv(
+            PACKAGE_DIRECTORY + "/data/fermentation_hplc.csv"
+        )
+        return fermentation_spectra, fermentation_hplc
+
+    if set_output == "polars":
+        fermentation_spectra = pl.read_csv(
+            PACKAGE_DIRECTORY + "/data/fermentation_spectra.csv"
+        )
+        fermentation_hplc = pl.read_csv(
+            PACKAGE_DIRECTORY + "/data/fermentation_hplc.csv"
+        )
+        return fermentation_spectra, fermentation_hplc
+
+    else:
+        raise ValueError(
+            "Invalid value for set_output. Please use 'pandas' or 'polars'."
+        )
+
+
+def load_coffee(set_output="pandas"):
     """
-    Loads the coffee dataset. This data corresponds to a coffee spectra from three different origins 
+    Loads the coffee dataset. This data corresponds to a coffee spectra from three different origins
     measured off-line using attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR).
 
+    Arguments
+    -------
+    set_output: str, default='pandas'
+        The output format of the data. It can be 'pandas' or 'polars'. If 'polars', the data is returned as a polars DataFrame.
+
     Returns
     -------
     coffee_spectra: pd.DataFrame A pandas DataFrame containing the coffee spectra.
     coffee_labels: pd.DataFrame A pandas DataFrame containing the corresponding labels.
     """
-
-    coffee_spectra = pd.read_csv(PACKAGE_DIRECTORY + "/data/coffee_spectra.csv")
-    coffee_labels = pd.read_csv(PACKAGE_DIRECTORY + "/data/coffee_labels.csv")
-
-    return coffee_spectra, coffee_labels
\ No newline at end of file
+    if set_output == "pandas":
+        coffee_spectra = pd.read_csv(PACKAGE_DIRECTORY + "/data/coffee_spectra.csv")
+        coffee_labels = pd.read_csv(PACKAGE_DIRECTORY + "/data/coffee_labels.csv")
+        return coffee_spectra, coffee_labels
+    
+    if set_output == "polars":
+        coffee_spectra = pl.read_csv(PACKAGE_DIRECTORY + "/data/coffee_spectra.csv")
+        coffee_labels = pl.read_csv(PACKAGE_DIRECTORY + "/data/coffee_labels.csv")
+        return coffee_spectra, coffee_labels
+    
+    else:
+        raise ValueError(
+            "Invalid value for set_output. Please use 'pandas' or 'polars'."
+        )
\ No newline at end of file
diff --git a/chemotools/feature_selection/_range_cut.py b/chemotools/feature_selection/_range_cut.py
index 9ad63f5..f391a07 100644
--- a/chemotools/feature_selection/_range_cut.py
+++ b/chemotools/feature_selection/_range_cut.py
@@ -34,6 +34,8 @@ class RangeCut(BaseEstimator, SelectorMixin):
     end_index_ : int
         The index of the end of the range. It is -1 if the wavenumbers are not provided.
 
+    wavenuumbers_ : array-like
+        The cut wavenumbers of the input data.
 
     Methods
     -------
@@ -75,9 +77,11 @@ def fit(self, X: np.ndarray, y=None) -> "RangeCut":
         if self.wavenumbers is None:
             self.start_index_ = self.start
             self.end_index_ = self.end
+            self.wavenumbers_ = None
         else:
             self.start_index_ = self._find_index(self.start)
             self.end_index_ = self._find_index(self.end)
+            self.wavenumbers_ = self.wavenumbers[self.start_index_ : self.end_index_]
 
         return self
     
diff --git a/docs/variable_selection.md b/docs/feature_selection.md
similarity index 73%
rename from docs/variable_selection.md
rename to docs/feature_selection.md
index 9e6e104..7ef4a1d 100644
--- a/docs/variable_selection.md
+++ b/docs/feature_selection.md
@@ -1,13 +1,13 @@
 ---
-title: Variable selection
+title: Feature selection
 layout: default
 parent: Docs
 ---
 
-# __Variable selection__
-Variable selection is a preprocessing technique in spectroscopy that selects the most relevant variables. The following algorithms are available:
+# __Feature selection__
+Feature selection is a preprocessing technique in spectroscopy that selects the most relevant features. The following algorithms are available:
 - [Range cut](#range-cut)
-- [SelectFeatures](#range-cut-by-wavenumber)
+- [IndexSelector](#index-selector)
 
 {: .note }
 > The variable selection algorithms implemented in ```chemotools``` allow you to select a subset of variables/features from the spectra. They are not designed to find the most relevant variables/features for a given task. 
@@ -31,7 +31,7 @@ Range cut by index is a preprocessing technique in spectroscopy that selects all
 #### __Case 1: Range cut by index__
 
 ```python
-from chemotools.variable_selection import RangeCut
+from chemotools.feature_selection import RangeCut
 
 rcbi = RangeCut(0, 200)
 spectra_rcbi = rcbi.fit_transform(spectra)
@@ -41,20 +41,24 @@ spectra_rcbi = rcbi.fit_transform(spectra)
 
 
 ```python
-from chemotools.variable_selection import RangeCut
+from chemotools.feature_selection import RangeCut
 
 rcbw = RangeCut(950, 1100, wavenumbers=wn)
 spectra_rcbw = rcbw.fit_transform(spectra)
 ```
 
+After fitting the method with the wavenumbers, the selected wavenumbers can be accessed using the ```wavenumbers_``` attribute.
+
+
 ### __Plotting example__:
 
 <iframe src="figures/range_cut_by_index.html" width="800px" height="400px" style="border: none;"></iframe>
 
 <iframe src="figures/range_cut_by_wavenumber.html" width="800px" height="400px" style="border: none;"></iframe>
 
-## __SelectFeatures__
-SelectFeatures is a preprocessing technique in spectroscopy that selects the most relevant variables. The selected features do not need to be continuous in the spectra, but they can be located at different locations. The algorithm allows selecting the features by imputing a list of indices or wavenumbers.
+
+## __Index selector__
+IndexSelector is a preprocessing technique in spectroscopy that selects the most relevant variables. The selected features do not need to be continuous in the spectra, but they can be located at different locations. The algorithm allows selecting the features by imputing a list of indices or wavenumbers.
 
 ### __Arguments__:
 
@@ -72,9 +76,9 @@ In the example below, the selected wavenumbers ```wn_select``` are used to selec
 
 
 ```python
-from chemotools.variable_selection import SelectFeatures
+from chemotools.feature_selection import IndexSelector
 
-sfbw = SelectFeatures(features=wn_select,wavenumbers=wn)
+sfbw = IndexSelector(features=wn_select,wavenumbers=wn)
 spectra_sfbw = sfbw.fit_transform(spectra)
 ```
 
diff --git a/get-started/brewing_regressor.md b/get-started/brewing_regressor.md
index bee2245..46c5b56 100644
--- a/get-started/brewing_regressor.md
+++ b/get-started/brewing_regressor.md
@@ -10,13 +10,16 @@ nav_order: 4
 
 ## What will you learn?
 
-- [Get familiar with the Fermentation dataset](#introduction)
-- [Load the fermentation dataset](#loading-the-training-dataset)
-- [Explore the fermentation dataset](#exploring-the-training-dataset)
-- [Visualize the fermentation dataset](#visualizing-the-training-dataset)
-- [Preprocess the spectra using pipelines](#preprocessing-the-training-spectra)
-- [Train a PLS model](#training-a-pls-model)
-- [Apply the model to the testing dataset](#applying-the-model-to-the-testing-dataset)
+- [__Brewing a PLS regressor__](#brewing-a-pls-regressor)
+  - [What will you learn?](#what-will-you-learn)
+  - [__Introduction__](#introduction)
+  - [__Loading the training dataset__](#loading-the-training-dataset)
+  - [__Exploring the training dataset__](#exploring-the-training-dataset)
+  - [__Visualizing the training dataset__](#visualizing-the-training-dataset)
+  - [__Preprocessing the training spectra__](#preprocessing-the-training-spectra)
+  - [__Training a PLS model__](#training-a-pls-model)
+  - [__Applying the model to the testing dataset__](#applying-the-model-to-the-testing-dataset)
+  - [__Recap__](#recap)
 
 ## __Introduction__
 Welcome to the world of spectroscopic data analysis, where we provide you with a unique insight into lignocellulosic ethanol fermentation in real-time. Our dataset comprises spectra obtained through attenuated total reflectance, mid-infrared (ATR-MIR) spectroscopy, combined with high-performance liquid chromatography (HPLC) reference data to ensure precision and accuracy.
@@ -47,6 +50,10 @@ The ```load_fermentation_train()``` function returns two ```pandas.DataFrame```:
 
 - ```hplc```: AHere, you'll find HPLC measurements, specifically glucose concentrations (in g/L), stored in a single column labeled ```glucose```.
 
+{: .highlight }
+> If you are interested in working with ```polars.DataFrame``` you can simply use  ```load_fermentation_train(set_output="polars")``` (chemotools>=0.1.5). Note that if you choose to work with ```polars.DataFrame``` the wavenumbers are given in the column names as ```str``` and not as ```float```. This is because ```polars``` does not support column names with types other than ```str```. To extract the wavenumbers as ```float``` from the ```polars.DataFrame``` you can use the ```df.columns.to_numpy(dtype=np.float64)``` method.
+
+
 ## __Exploring the training dataset__
 
 Before diving into data modeling, it's essential to get familiar with your data. Start by answering basic questions: _How many samples are there?_, and _how many wavenumbers are available?_
@@ -99,7 +106,7 @@ To better understand our dataset, we employ visualization. We will plot the trai
 Up until now, we have used ```pandas.DataFrame``` to represent the dataset. ```pandas.DataFrame``` are great for storing and manipulating many large datasets. However, I often find more convenient to use ```numpy.ndarray``` to work with spectral data. Therefore, we will convert the ```pandas.DataFrame``` to ```numpy.ndarray``` using the ```pandas.DataFrame.to_numpy()``` method.
 
 {: .note }
-> Pandas lover 🐼 ❤️? No problem! ```chemotools``` also supports working with ```pandas.DataFrame``` by implementing the latest ```set_output()``` API from ```scikit-learn```. If you are more interested in working with ```pandas```, take a look at the documentation [here](https://paucablop.github.io/chemotools/get-started/scikit_learn_integration.html#working-with-pandas-dataframes).
+> Pandas 🐼 or polars 🐻‍❄️ lover ❤️? No problem! ```chemotools``` also supports working with ```pandas.DataFrame``` or ```polars.DataFrame``` by implementing the latest ```set_output()``` API from ```scikit-learn```. If you are more interested in working with ```pandas``` or ```polars```, take a look at the documentation [here](https://paucablop.github.io/chemotools/get-started/scikit_learn_integration.html#working-with-dataframes).
 
 So our first step will be to transform our ```pandas.DataFrame``` to ```numpy.ndarray```:
 
@@ -113,7 +120,7 @@ spectra_np = spectra.to_numpy()
 wavenumbers = spectra.columns.to_numpy(dtype=np.float64)
 
 # Convert the hplc pandas.DataFrame to numpy.ndarray
-hplc = hplc.to_numpy()
+hplc_np = hplc.to_numpy()
 ```
 
 Now that we have our data in the right format, we can start plotting. We will define a function to plot the spectra, where each spectrum will be color-coded according to its glucose concentration. We will use the ```matplotlib.colors.Normalize``` class to normalize the glucose concentrations between 0 and 1. Then, we will use the ```matplotlib.cm.ScalarMappable``` class to create a colorbar.
@@ -128,7 +135,7 @@ def plot_spectra(spectra: np.ndarray, wavenumbers: np.ndarray, hplc: np.ndarray)
     cmap = plt.get_cmap("jet")
 
     # Define a normalization function to scale glucose concentrations between 0 and 1
-    norm = Normalize(vmin=hplc.min(), vmax=hplc.max())
+    normalize = Normalize(vmin=hplc.min(), vmax=hplc.max())
     colors = [cmap(normalize(value)) for value in hplc]
 
     # Plot the spectra
@@ -152,7 +159,7 @@ def plot_spectra(spectra: np.ndarray, wavenumbers: np.ndarray, hplc: np.ndarray)
 Then, we can use this function to plot the training dataset:
 
 ```python
-plot_spectra(spectra, hplc)
+plot_spectra(spectra_np, wavenumbers, hplc_np)
 ```
 
 which should result in the following plot:
@@ -169,7 +176,7 @@ Now that you've explored the dataset, it's time to preprocess the spectral data.
 
 We will preprocess the spectra using the following steps:
 
-- __[Range Cut](https://paucablop.github.io/chemotools/docs/variable_selection.html#range-cut)__: to remove the wavenumbers outside the range between 950 and 1550 cm-1.
+- __[Range Cut](https://paucablop.github.io/chemotools/docs/feature_selection.html#range-cut)__: to remove the wavenumbers outside the range between 950 and 1550 cm-1.
 
 - __[Linear Correction](https://paucablop.github.io/chemotools/docs/baseline.html#linear-baseline-correction)__: to remove the linear baseline shift. 
 
@@ -182,7 +189,7 @@ We will chain the preprocessing steps using the [```make_pipeline()```](https://
 
 
 ```python
-from chemotools.variable_selection import RangeCut
+from chemotools.feature_selection import RangeCut
 from chemotools.baseline import LinearCorrection
 from chemotools.derivative import SavitzkyGolay
 
@@ -191,7 +198,7 @@ from sklearn.pipeline import make_pipeline
 
 # create a pipeline that scales the data
 preprocessing = make_pipeline(
-    RangeCut(start=950, end=1500, wavelength=wavenumbers),
+    RangeCut(start=950, end=1500, wavenumbers=wavenumbers),
     LinearCorrection(),
     SavitzkyGolay(window_size=15, polynomial_order=2, derivate_order=1),
     StandardScaler(with_std=False)
@@ -208,9 +215,8 @@ Finally, we can plot the preprocessed spectra:
 
 ```python
 # get the wavenumbers after the range cut
-start_index = preprocessing.named_steps['rangecut'].start
-end_index = preprocessing.named_steps['rangecut'].end
-wavenumbers_cut = wavenumbers[start_index:end_index]
+wavenumbers_cut = preprocessing.named_steps['rangecut'].wavenumbers_
+
 
 # plot the preprocessed spectra
 plot_spectra(spectra_preprocessed, wavenumbers_cut, hplc_np)
@@ -295,7 +301,7 @@ hplc_pred = pls.predict(spectra_preprocessed)
 
 # plot the predictions
 fig, ax = plt.subplots(figsize=(4, 4))
-ax.scatter(hplc_np, predictions, color='blue')
+ax.scatter(hplc_np, hplc_pred, color='blue')
 ax.plot([0, 40], [0, 40], color='magenta')
 ax.set_xlabel('Measured glucose (g/L)')
 ax.set_ylabel('Predicted glucose (g/L)')
@@ -352,12 +358,12 @@ Now we can compare the predicted glucose concentrations with the off-line HPLC m
 
 ```python
 # make linspace of length of predictoins
-time = np.linspace(0, len(predictions_test), len(predictions_test),) * 1.25 / 60
+time = np.linspace(0, len(glucose_test_pred), len(glucose_test_pred),) * 1.25 / 60
 
 # plot the predictions
 fig, ax = plt.subplots(figsize=(10, 4))
 
-ax.plot(time, predictions_test,  color='blue', label='Predicted')
+ax.plot(time, glucose_test_pred,  color='blue', label='Predicted')
 ax.plot(hplc_test.index, hplc_test['glucose']+4, 'o', color='red', label='Measured')
 ax.set_xlabel('Time (h)')
 ax.set_ylabel('Glucose (g/L)')
diff --git a/get-started/coffee_spectra_classifier.md b/get-started/coffee_spectra_classifier.md
index e5bddf1..2edf008 100644
--- a/get-started/coffee_spectra_classifier.md
+++ b/get-started/coffee_spectra_classifier.md
@@ -10,12 +10,15 @@ nav_order: 5
 
 ## What will you learn?
 
-- [The coffee dataset](#the-coffee-dataset-🌍)
-- [Importing the data](#importing-the-data)
-- [Explore, plot and color](#explore-plot-and-color)
-- [Exploring the data](#exploring-the-data-🤓)
-- [Preprocessing the spectra](#preprocessing-the-spectra-🌊)
-- [Modelling the data](#modelling-the-data)
+- [__Coffee Spectra Classifier__](#coffee-spectra-classifier)
+  - [What will you learn?](#what-will-you-learn)
+  - [__Unlocking the Secrets of Coffee: A Spectral Journey ☕__](#unlocking-the-secrets-of-coffee-a-spectral-journey-)
+    - [__The Coffee Dataset 🌍__](#the-coffee-dataset-)
+  - [__Importing the data__](#importing-the-data)
+  - [__Explore, plot and color__](#explore-plot-and-color)
+  - [__Preprocessing the spectra  🌊__](#preprocessing-the-spectra--)
+  - [__Modelling the data__](#modelling-the-data)
+  - [__Recap__](#recap)
 
 ## __Unlocking the Secrets of Coffee: A Spectral Journey ☕__
 
@@ -41,7 +44,7 @@ So, grab your favorite coffee mug, prepare to delve into the world of data-drive
 
 ## __Importing the data__
 
-Fantastic! Now that we've set the stage with our coffee dataset, it's time to take the plunge into the rich world of data analysis. No need to worry about complicated data wrangling – with ```chemotools``` we've made it effortless for you. We've gracefully loaded our coffee spectra into a sleek and ready-to-explore ```pandas.DataFrame```. Let's start brewing some data magic! ☕🔮📊. 
+Fantastic! Now that we've set the stage with our coffee dataset, it's time to take the plunge into the rich world of data analysis. No need to worry about complicated data wrangling – with ```chemotools``` we've made it effortless for you. We've gracefully loaded our coffee spectra into a sleek and ready-to-explore ```pandas.DataFrame``` or ```polars.DataFrame```, your choice! For this example, we will use ```pandas.DataFrame```. Let's start brewing some data magic! ☕🔮📊. 
 
 
 ```python
@@ -55,6 +58,9 @@ The ```load_coffee()``` function returns two variables: ```spectra``` and ```lab
 - ```spectra```: A ```pandas.DataFrame``` containing the spectra of the coffee samples as rows.
 - ```labels```: A ```pandas.DataFrame``` containing the origin of each sample.
 
+{: .highlight }
+> If you are interested in working with ```polars.DataFrame``` you can simply use the  ```load_coffee(set_output="polars")```  (```chemotools```>=0.1.5).
+
 ## __Explore, plot and color__
 
 Before we delve deep into our coffee data analysis, let's quickly size up our datasets. Understanding their dimensions is our first step to uncovering insights. Let's get a snapshot of the data sizes and kickstart our analysis. Ready? Let's roll! ☕📏📊
@@ -160,7 +166,7 @@ We will build the preprocessing steps in a pipeline using the ``` make_pipeline(
 
 - __[Derivative](https://paucablop.github.io/chemotools/docs/derivative.html#savitzky-golay-derivative)__ to remove both additive and multiplicative scattering effects.
 
-- __[Range cut](https://paucablop.github.io/chemotools/docs/variable_selection.html#range-cut)__ to select the most relevant wavenumbers.
+- __[Range cut](https://paucablop.github.io/chemotools/docs/feature_selection.html#range-cut)__ to select the most relevant wavenumbers.
 
 - __[Standardize](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)__ remove the mean from the dataset.
 
@@ -170,7 +176,7 @@ from sklearn.preprocessing import StandardScaler
 
 from chemotools.derivative import SavitzkyGolay
 from chemotools.scatter import StandardNormalVariate
-from chemotools.variable_selection import RangeCut
+from chemotools.feature_selection import RangeCut
 
 pipeline = make_pipeline(
     StandardNormalVariate(),
diff --git a/get-started/explore_our_exciting_datasets.md b/get-started/explore_our_exciting_datasets.md
index d2ba059..fe64c6a 100644
--- a/get-started/explore_our_exciting_datasets.md
+++ b/get-started/explore_our_exciting_datasets.md
@@ -29,7 +29,9 @@ For those curious minds, you can find more about the Fermentation Dataset in the
 
 #### __📚 THE TRAIN SET: Start Your Training Adventure__
 
-The train set boasts 21 synthetic spectra paired with their reference glucose concentrations, measured by high-performance liquid chromatography (HPLC). Ready to embark on your training journey? You can load the train set with a single command:
+The train set boasts 21 synthetic spectra paired with their reference glucose concentrations, measured by high-performance liquid chromatography (HPLC). Ready to embark on your training journey? You can load the train set as a ```pandas.DataFrame``` or as a ```polars.DataFrame``` with a single command:
+
+- __Load as```pandas.DataFrame```__:
 
 ```python
 from chemotools.datasets import load_fermentation_train
@@ -37,6 +39,18 @@ from chemotools.datasets import load_fermentation_train
 X_train, y_train = load_fermentation_train()
 ```
 
+- __Load as```polars.DataFrame```__:
+
+```python
+from chemotools.datasets import load_fermentation_train
+
+X_train, y_train = load_fermentation_train(set_output="polars")
+```
+
+{: .highlight}
+> Polars is supported in ```chemotools```>=0.1.5
+
+
 {: .note}
 > Want to master the art of building a PLS model using the Fermentation Dataset? 📝 [Dive into our Training Guide](https://paucablop.github.io/chemotools/get-started/brewing_regressor.html).
 
@@ -46,24 +60,47 @@ The test set takes you on a real-time adventure with over 1000 spectra collected
 
 Ready for this real-time exploration? Load the test set like a pro:
 
+- __Load as```pandas.DataFrame```__:
+  
 ```python
 from chemotools.datasets import load_fermentation_test
 
 X_test, y_test = load_fermentation_test()
 ```
 
+- __Load as```polars.DataFrame```__:
+  
+```python
+from chemotools.datasets import load_fermentation_test
+
+X_test, y_test = load_fermentation_test(set_output="polars")
+```
+
+{: .highlight }
+> Note that the wavenumbers are stored as the column names in both the ```pandas.DataFrame``` and the ```polars.DataFrame```. However, while in a ```pandas.DataFrame``` the column names can be of type ```float```, in a ```polars.DataFrame``` the column names must be of type ```str```. 
+
 ## __☕ The Coffee Dataset: A Global Coffee Journey 🌍__
 
 The Coffee Dataset invites you to embark on a journey through the world of coffee. These captivating spectra are collected from a rich diversity of coffee samples, each originating from a different country. The magic happens with attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR).
 
-Feeling the coffee buzz? You can load the Coffee Dataset with ease:
+Feeling the coffee buzz? You can load the Coffee Dataset with ease as a ```pandas.DataFrame``` or as a ```polars.DataFrame```.
 
+- __Load as```pandas.DataFrame```__:
+  
 ```python
 from chemotools.datasets import load_coffee
 
 spectra, labels = load_coffee()
 ```
 
+- __Load as```polars.DataFrame```__:
+  
+```python
+from chemotools.datasets import load_coffee
+
+spectra, labels = load_coffee(set_output="polars")
+```
+
 {: .note}
 > Ready to brew up some knowledge and build a PLS-DA classification model using the Coffee Dataset? 📚 [Get started with our Training Guide.](https://paucablop.github.io/chemotools/get-started/coffee_spectra_classifier.html)
 
diff --git a/get-started/scikit_learn_integration.md b/get-started/scikit_learn_integration.md
index 0909925..02202e5 100644
--- a/get-started/scikit_learn_integration.md
+++ b/get-started/scikit_learn_integration.md
@@ -11,7 +11,7 @@ This page shows how to use ```chemotools``` in combination with ```scikit-learn`
 
 - [Working with single spectra](#working-with-single-spectra)
 - [Working with pipelines](#working-with-pipelines)
-- [Working with pandas DataFrames](#working-with-pandas-dataframes)
+- [Working with pandas DataFrames](#working-with-dataframes-🐼-and-🐻‍❄️)
 - [Persisting your models](#persisting-your-models)
 
 ## __Working with single spectra__
@@ -126,8 +126,8 @@ The preprocessed spectra produced by the previous pipeline is shown in the figur
 > Notice that in the traditional workflow, the different preprocessing objects had to be persisted individually. In the pipeline workflow, the entire pipeline can be persisted and deployed to a production environment. See the [Persisting your models](#persisting-your-models) section for more information.
 
 
-## __Working with pandas DataFrames__
-For the ```pandas.DataFrame``` lovers. By default, all ```scikit-learn``` and ```chemotools``` transformers output ```numpy.ndarray```. However, now it is possible to configure your ```chemotools``` preprocessing methods to produce ```pandas.DataFrame``` objects as output. This is possible after implementing the new ```set_output()``` API from ```scikit-learn```>= 1.2.2 ([documentation](https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html)). The same API implemented in other ```scikit-learn``` preprocessing methods like the ```StandardScaler()``` is now available for the ```chemotools``` transformers. 
+## __Working with DataFrames (🐼 and 🐻‍❄️ )__
+For the ```pandas.DataFrame``` and ```polars.DataFrame``` lovers. By default, all ```scikit-learn``` and ```chemotools``` transformers output ```numpy.ndarray```. However, now it is possible to configure your ```chemotools``` preprocessing methods to produce either a ```pandas.DataFrame``` or a ```polars.DataFrame``` objects as output. This is possible after implementing the new ```set_output()``` API from ```scikit-learn``` (>= 1.2.2 for ```pandas``` and >= 1.4.0 for ```polars```)([documentation](https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html)). The same API implemented in other ```scikit-learn``` preprocessing methods like the ```StandardScaler()``` is now available for the ```chemotools``` transformers. 
 
 {: .note }
 > From version 0.1.3, the ```set_output()``` is available for all ```chemotools``` functions!
@@ -150,13 +150,13 @@ spectra = pd.read_csv('data/spectra.csv', index_col=0)
 
 The ```spectra``` variable is a ```pandas.DataFrame``` object with the indices representing the sample names and the columns representing the wavenumbers. The first 5 rows of the ```spectra``` DataFrame look like this:
 
-|    |    900.0 |    901.0 |    903.0 |    904.0 |    905.0 |    906.0 |    908.0 |    909.0 |    910.0 |
-|---:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
-|  0 | 0.246749 | 0.268549 | 0.279464 | 0.280701 | 0.292982 | 0.288912 | 0.297167 | 0.310435 | 0.325145 |
-|  1 | 0.235092 | 0.249278 | 0.25094  | 0.251326 | 0.266078 | 0.263885 | 0.279901 | 0.295895 | 0.297663 |
-|  2 | 0.227894 | 0.223541 | 0.226005 | 0.23621  | 0.249276 | 0.26032  | 0.258642 | 0.282584 | 0.285163 |
-|  3 | 0.204115 | 0.213624 | 0.220228 | 0.222264 | 0.225996 | 0.232336 | 0.235273 | 0.261938 | 0.26663  |
-|  4 | 0.195615 | 0.195829 | 0.203789 | 0.220114 | 0.233223 | 0.240248 | 0.246378 | 0.261398 | 0.267355 |
+|      |    900.0 |    901.0 |    903.0 |    904.0 |    905.0 |    906.0 |    908.0 |    909.0 |    910.0 |
+| ---: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: |
+|    0 | 0.246749 | 0.268549 | 0.279464 | 0.280701 | 0.292982 | 0.288912 | 0.297167 | 0.310435 | 0.325145 |
+|    1 | 0.235092 | 0.249278 |  0.25094 | 0.251326 | 0.266078 | 0.263885 | 0.279901 | 0.295895 | 0.297663 |
+|    2 | 0.227894 | 0.223541 | 0.226005 |  0.23621 | 0.249276 |  0.26032 | 0.258642 | 0.282584 | 0.285163 |
+|    3 | 0.204115 | 0.213624 | 0.220228 | 0.222264 | 0.225996 | 0.232336 | 0.235273 | 0.261938 |  0.26663 |
+|    4 | 0.195615 | 0.195829 | 0.203789 | 0.220114 | 0.233223 | 0.240248 | 0.246378 | 0.261398 | 0.267355 |
 
 
 #### __2. Create a ```chemotools``` preprocessing object and set the output to ```pandas```.__
@@ -171,6 +171,9 @@ The ```set_output()``` method accepts the following arguments:
 
 - ```transform```: The output format. Can be ```'pandas'``` or ```'default'``` (the default format will output a ```numpy.ndarray```).
 
+{: .highlight }
+> If you wanted to set the output to ```polars``` you would use ```transform='polars'``` in the ```set_output()``` method (```AirPLS().set_output(transform='polars')```).
+
 
 #### __3. Fit and transform the spectra__
 
@@ -186,13 +189,13 @@ The output of the ```fit_transform()``` method is now a ```pandas.DataFrame``` o
 
 The ```spectra_airpls``` DataFrame has the following structure:
 
-|    |       x0 |       x1 |       x2 |       x3 |       x4 |       x5 |       x6 |       x7 |       x8 |       x9 |
-|---:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
-|  0 | 0.210838 | 0.213002 | 0.217275 | 0.222833 | 0.229342 | 0.236683 | 0.245315 | 0.254254 | 0.263244 | 0.272121 |
-|  1 | 0.219816 | 0.220637 | 0.223478 | 0.227481 | 0.233518 | 0.240035 | 0.247666 | 0.256066 | 0.264704 | 0.273879 |
-|  2 | 0.220096 | 0.221503 | 0.224515 | 0.22905  | 0.23486  | 0.242032 | 0.250077 | 0.25948  | 0.268111 | 0.276561 |
-|  3 | 0.211932 | 0.213675 | 0.216953 | 0.222211 | 0.22891  | 0.235941 | 0.243654 | 0.252518 | 0.261452 | 0.270276 |
-|  4 | 0.212528 | 0.21408  | 0.217522 | 0.222005 | 0.228657 | 0.236576 | 0.244935 | 0.253593 | 0.262239 | 0.271826 |
+|      |       x0 |       x1 |       x2 |       x3 |       x4 |       x5 |       x6 |       x7 |       x8 |       x9 |
+| ---: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: |
+|    0 | 0.210838 | 0.213002 | 0.217275 | 0.222833 | 0.229342 | 0.236683 | 0.245315 | 0.254254 | 0.263244 | 0.272121 |
+|    1 | 0.219816 | 0.220637 | 0.223478 | 0.227481 | 0.233518 | 0.240035 | 0.247666 | 0.256066 | 0.264704 | 0.273879 |
+|    2 | 0.220096 | 0.221503 | 0.224515 |  0.22905 |  0.23486 | 0.242032 | 0.250077 |  0.25948 | 0.268111 | 0.276561 |
+|    3 | 0.211932 | 0.213675 | 0.216953 | 0.222211 |  0.22891 | 0.235941 | 0.243654 | 0.252518 | 0.261452 | 0.270276 |
+|    4 | 0.212528 |  0.21408 | 0.217522 | 0.222005 | 0.228657 | 0.236576 | 0.244935 | 0.253593 | 0.262239 | 0.271826 |
 
 ### __Example 2: Using the ```set_output()``` API with a pipeline__
 
@@ -213,6 +216,9 @@ pipeline.set_output(transform="pandas")
 output = pipeline.fit_transform(spectra)
 ```
 
+{: .highlight }
+> If you wanted to set the output to ```polars``` you would use ```transform='polars'``` in the ```set_output()``` method (```pipeline.set_output(transform='polars')```).
+
 ## __Persisting your models__
 
 In the previous sections, we saw how to use ```chemotools``` in combination with ```scikit-learn``` to preprocess your data and make predictions. However, in a real-world scenario, you would like to persist your preprocessing pipelines and models to deploy it to a production environment. In this section, we will show two ways to persist your models:
diff --git a/releases/0.1.4.md b/releases/0.1.4.md
new file mode 100644
index 0000000..310846a
--- /dev/null
+++ b/releases/0.1.4.md
@@ -0,0 +1,15 @@
+---
+title: v0.1.4
+layout: default
+parent: Releases
+---
+
+# __[Release v0.1.4](https://github.com/paucablop/chemotools/releases/tag/v0.1.4)__
+
+## __What's new? 🎉🎉__
+
+## __Improvements ✨✨__
+
+Range Cut function, now incorporates an attribute wavenumbers_ that contains the cut wavenumbers
+
+## __Bug fixes 🐛🐛__
diff --git a/releases/0.1.5.md b/releases/0.1.5.md
new file mode 100644
index 0000000..3670309
--- /dev/null
+++ b/releases/0.1.5.md
@@ -0,0 +1,15 @@
+---
+title: v0.1.5
+layout: default
+parent: Releases
+---
+
+# __[Release v0.1.5](https://github.com/paucablop/chemotools/releases/tag/v0.1.5)__
+
+## __What's new? 🎉🎉__
+
+## __Improvements ✨✨__
+
+Polars support for datasets and for all functions. Now you can load the datasets as ```polars.DataFrame``` and use the functions with ```polars.DataFrame```.
+
+## __Bug fixes 🐛🐛__
diff --git a/requirements.txt b/requirements.txt
index c9045e8..1b631ca 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
 numpy>=1.24.1
 pandas>=1.3.4
-scikit-learn>=1.2.0
-scipy>=1.11.0
+polars>=0.20.0
+pyarrow>=15.0.0
+scikit-learn>=1.4.0
diff --git a/setup.py b/setup.py
index b8feccf..b1590bd 100644
--- a/setup.py
+++ b/setup.py
@@ -27,8 +27,10 @@
     install_requires=[
         "numpy",
         "pandas",
+        "polars",
+        "pyarrow",
         "scipy",
-        "scikit-learn",
+        "scikit-learn>=1.4.0",
     ],
     include_package_data=True,
     package_data={'': ['tests/resources/*.csv',
diff --git a/tests/test_datasets.py b/tests/test_datasets.py
index 5251664..b76b358 100644
--- a/tests/test_datasets.py
+++ b/tests/test_datasets.py
@@ -1,9 +1,15 @@
 import pandas as pd
+import polars as pl
+import pytest
 
-from chemotools.datasets import load_coffee, load_fermentation_test, load_fermentation_train
+from chemotools.datasets import (
+    load_coffee,
+    load_fermentation_test,
+    load_fermentation_train,
+)
 
 
-def test_load_coffee():
+def test_load_coffee_pandas():
     # Arrange
 
     # Act
@@ -16,7 +22,28 @@ def test_load_coffee():
     assert isinstance(coffee_labels, pd.DataFrame)
 
 
-def test_load_fermentation_test():
+def test_load_coffee_polars():
+    # Arrange
+
+    # Act
+    coffee_spectra, coffee_labels = load_coffee(set_output="polars")
+
+    # Assert
+    assert coffee_spectra.shape == (60, 1841)
+    assert coffee_labels.shape == (60, 1)
+    assert isinstance(coffee_spectra, pl.DataFrame)
+    assert isinstance(coffee_labels, pl.DataFrame)
+
+
+def test_load_coffee_exception():
+    # Arrange
+
+    # Act and Assert
+    with pytest.raises(ValueError):
+        coffee_spectra, coffee_labels = load_coffee(set_output="plars")
+
+
+def test_load_fermentation_test_pandas():
     # Arrange
 
     # Act
@@ -28,7 +55,29 @@ def test_load_fermentation_test():
     assert isinstance(test_spectra, pd.DataFrame)
     assert isinstance(test_hplc, pd.DataFrame)
 
-def test_load_fermentation_train():
+
+def test_load_fermentation_test_polars():
+    # Arrange
+
+    # Act
+    test_spectra, test_hplc = load_fermentation_test(set_output="polars")
+
+    # Assert
+    assert test_spectra.shape == (1629, 1047)
+    assert test_hplc.shape == (34, 6)
+    assert isinstance(test_spectra, pl.DataFrame)
+    assert isinstance(test_hplc, pl.DataFrame)
+
+
+def test_load_fermentation_test_exception():
+    # Arrange
+
+    # Act and Assert
+    with pytest.raises(ValueError):
+        test_spectra, test_hplc = load_fermentation_test(set_output="plars")
+
+
+def test_load_fermentation_train_pandas():
     # Arrange
 
     # Act
@@ -40,4 +89,23 @@ def test_load_fermentation_train():
     assert isinstance(train_spectra, pd.DataFrame)
     assert isinstance(train_hplc, pd.DataFrame)
 
-    
+
+def test_load_fermentation_train_polars():
+    # Arrange
+
+    # Act
+    train_spectra, train_hplc = load_fermentation_train(set_output="polars")
+
+    # Assert
+    assert train_spectra.shape == (21, 1047)
+    assert train_hplc.shape == (21, 1)
+    assert isinstance(train_spectra, pl.DataFrame)
+    assert isinstance(train_hplc, pl.DataFrame)
+
+
+def test_load_fermentation_train_exception():
+    # Arrange
+
+    # Act and Assert
+    with pytest.raises(ValueError):
+        train_spectra, train_hplc = load_fermentation_train(set_output="plars")
diff --git a/tests/test_functionality.py b/tests/test_functionality.py
index b3f05d4..e843216 100644
--- a/tests/test_functionality.py
+++ b/tests/test_functionality.py
@@ -1,5 +1,6 @@
 import numpy as np
 import pandas as pd
+import polars as pl
 import pytest
 
 from chemotools._runtime import PENTAPY_AVAILABLE
@@ -665,9 +666,10 @@ def test_range_cut_by_wavenumber_with_list():
 
     # Assert
     assert np.allclose(spectrum_corrected[0], spectrum[0][1:7], atol=1e-8)
+    assert range_cut.wavenumbers_ == [2, 3, 4, 5, 6, 7]
 
 
-def test_range_cut_by_wavenumber_with_dataframe():
+def test_range_cut_by_wavenumber_with_pandas_dataframe():
     # Arrange
     wavenumbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
     spectrum = pd.DataFrame(np.array([[10, 12, 14, 16, 14, 12, 10, 12, 14, 16]]))
@@ -682,6 +684,19 @@ def test_range_cut_by_wavenumber_with_dataframe():
     assert type(spectrum_corrected) == pd.DataFrame
 
 
+def test_range_cut_by_wavenumber_with_polars_dataframe():
+    # Arrange
+    wavenumbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+    spectrum = pl.DataFrame(np.array([[10, 12, 14, 16, 14, 12, 10, 12, 14, 16]]))
+    range_cut = RangeCut(start=2.5, end=7.9, wavenumbers=wavenumbers).set_output(transform='polars')
+
+    # Act
+    spectrum_corrected = range_cut.fit_transform(spectrum)
+
+    # Assert
+    assert type(spectrum_corrected) == pl.DataFrame
+
+
 def test_robust_normal_variate():
     # Arrange
     spectrum = np.array([2, 3.5, 5, 27, 8, 9]).reshape(1, -1)